From d37aa672667bbd7e72ea82a58ac10e7eb0fdee1c Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Mon, 28 Aug 2023 13:46:15 +0000 Subject: [PATCH 01/46] initial demo attack on clip Signed-off-by: GiulioZizzo --- clip_initial_attack.py | 84 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 clip_initial_attack.py diff --git a/clip_initial_attack.py b/clip_initial_attack.py new file mode 100644 index 0000000000..1c2f656c84 --- /dev/null +++ b/clip_initial_attack.py @@ -0,0 +1,84 @@ +import numpy as np + +from matplotlib import pyplot as plt +import torch + +MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) +STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) + +def norm_bound_eps(eps_bound=None): + if eps_bound is None: + eps_bound = np.asarray([8/255, 8/255, 8/255]) + eps_bound = np.abs(eps_bound /STD) + return eps_bound + +def attack_clip(): + from PIL import Image + import requests + + from transformers import CLIPProcessor, CLIPModel + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of a cat", "a photo of a dog"] + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + inputs = processor(text=text, images=image, return_tensors="pt", + padding=True) + original_image = inputs.pixel_values.clone().cpu().numpy() + + pixel_values = inputs.pixel_values.requires_grad_(True) + attention_mask = inputs.attention_mask + input_ids = inputs.input_ids + + init_max = torch.max(pixel_values) + init_min = torch.min(pixel_values) + + labels = torch.tensor(np.asarray([0])) + lossfn = torch.nn.CrossEntropyLoss() + eps = norm_bound_eps() + + mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() + maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + + outputs = model(pixel_values=pixel_values, attention_mask=attention_mask, input_ids=input_ids) + print('Original class: ', text[torch.argmax(outputs.logits_per_image)]) + + for i in range(10): + pixel_values = pixel_values.requires_grad_(True) + + outputs = model(pixel_values=pixel_values, attention_mask=attention_mask, input_ids=input_ids) + logits_per_image = outputs.logits_per_image # image-text similarity score + + loss = lossfn(logits_per_image, labels) + loss.backward() + + with torch.no_grad(): + sign = torch.sign(pixel_values.grad) + pixel_values = torch.clamp(pixel_values + sign * 0.1, min=init_min, max=init_max) + pixel_values = torch.clamp(pixel_values, min=mins, max=maxs) + + model.zero_grad() + + outputs = model(pixel_values=pixel_values, attention_mask=attention_mask, input_ids=input_ids) + print('Attacked class: ', text[torch.argmax(outputs.logits_per_image)]) + + for i, (channel_std, channel_mean) in enumerate(zip(STD, MEAN)): + pixel_values[:, i, :, :] = pixel_values[:, i, :, :] * channel_std + pixel_values[:, i, :, :] = pixel_values[:, i, :, :] + channel_mean + + original_image[:, i, :, :] = original_image[:, i, :, :] * channel_std + original_image[:, i, :, :] = original_image[:, i, :, :] + channel_mean + + + pixel_values = pixel_values.cpu().numpy() + pixel_values = np.squeeze(np.transpose(pixel_values, (0, 2, 3, 1))) + original_image = np.squeeze(np.transpose(original_image, (0, 2, 3, 1))) + + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(pixel_values) + ax2.imshow(original_image) + plt.show() + +attack_clip() \ No newline at end of file From a1f3b6a7732651497ca527336b9fee5fe97c9cfc Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Mon, 18 Sep 2023 19:10:13 +0100 Subject: [PATCH 02/46] initial POC of attacking CLIP with ART tools Signed-off-by: GiulioZizzo --- art/attacks/evasion/fast_gradient.py | 9 +- art/estimators/__init__.py | 1 + art/estimators/hf_mm/__init__.py | 2 + art/estimators/hf_mm/hf_inputs.py | 87 +++++ art/estimators/hf_mm/huggingface_mm.py | 312 ++++++++++++++++++ art/utils.py | 11 +- clip_dev.py | 64 ++++ .../attacks/evasion/test_multimodal_attack.py | 265 +++++++++++++++ 8 files changed, 747 insertions(+), 4 deletions(-) create mode 100644 art/estimators/hf_mm/__init__.py create mode 100644 art/estimators/hf_mm/hf_inputs.py create mode 100644 art/estimators/hf_mm/huggingface_mm.py create mode 100644 clip_dev.py create mode 100644 tests/attacks/evasion/test_multimodal_attack.py diff --git a/art/attacks/evasion/fast_gradient.py b/art/attacks/evasion/fast_gradient.py index 85491c2630..38ee1e1b7e 100644 --- a/art/attacks/evasion/fast_gradient.py +++ b/art/attacks/evasion/fast_gradient.py @@ -24,6 +24,7 @@ from __future__ import absolute_import, division, print_function, unicode_literals import logging +from collections import UserDict from typing import Optional, Union, TYPE_CHECKING import numpy as np @@ -480,8 +481,12 @@ def _apply_perturbation( if self.estimator.clip_values is not None: clip_min, clip_max = self.estimator.clip_values if x.dtype == object: - for i_obj in range(x.shape[0]): - x[i_obj] = np.clip(x[i_obj], clip_min, clip_max) + if isinstance(x, UserDict): + for i_obj in range(x.shape[0]): + x[i_obj] = np.clip(x[i_obj]['pixel_values'].cpu().detach().numpy(), clip_min, clip_max) + else: + for i_obj in range(x.shape[0]): + x[i_obj] = np.clip(x[i_obj], clip_min, clip_max) else: x = np.clip(x, clip_min, clip_max) diff --git a/art/estimators/__init__.py b/art/estimators/__init__.py index f9a7d41928..190237ecbc 100644 --- a/art/estimators/__init__.py +++ b/art/estimators/__init__.py @@ -17,6 +17,7 @@ from art.estimators import certification from art.estimators import classification from art.estimators import encoding +from art.estimators import hf_mm from art.estimators import generation from art.estimators import object_detection from art.estimators import poison_mitigation diff --git a/art/estimators/hf_mm/__init__.py b/art/estimators/hf_mm/__init__.py new file mode 100644 index 0000000000..8f9068e553 --- /dev/null +++ b/art/estimators/hf_mm/__init__.py @@ -0,0 +1,2 @@ +from art.estimators.hf_mm.huggingface_mm import HFMMPyTorch +from art.estimators.hf_mm.hf_inputs import ARTInput diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py new file mode 100644 index 0000000000..c2ce2e83f7 --- /dev/null +++ b/art/estimators/hf_mm/hf_inputs.py @@ -0,0 +1,87 @@ +from collections import UserDict +import torch + + +class ARTInput(UserDict): + """ + Custom class to allow HF inputs which are in a dictionary to be compatible with ART. + Allows certain array-like functionality to be performed directly onto the input such as + some arithmetic operations (addition, subtraction), and python operations + such as slicing, reshaping, etc to be performed on the correct components of the HF input. + """ + dtype = object + shape = (1, 3, 224, 224) + ndim = 4 + + def __setitem__(self, key, value): + # print('key ', key) + # print('value ', value) + if isinstance(key, slice): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + original_shape = pixel_values.shape + with torch.no_grad(): + pixel_values[key] = value['pixel_values'] + super().__setitem__('pixel_values', pixel_values) + assert self['pixel_values'].shape == original_shape + + if isinstance(key, str): + super().__setitem__(key, value) + if key == 'pixel_values': + pixel_values = UserDict.__getitem__(self, 'pixel_values') + self.shape = pixel_values.shape + self.ndim = pixel_values.ndim + + if isinstance(key, int): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + original_shape = pixel_values.shape + if isinstance(value, ARTInput): + pixel_values[key] = value['pixel_values'] + else: + pixel_values[key] = torch.tensor(value) + self['pixel_values'] = pixel_values + assert self['pixel_values'].shape == original_shape + + def __getitem__(self, item): + if isinstance(item, slice): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + pixel_values = pixel_values[item] + output = ARTInput(**self) + output['pixel_values'] = pixel_values + return output + elif isinstance(item, int): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + pixel_values = pixel_values[item] + output = ARTInput(**self) + output['pixel_values'] = pixel_values + return output + + elif item in self.keys(): + return UserDict.__getitem__(self, item) + + def __add__(self, other): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + if isinstance(other, ARTInput): + pixel_values = pixel_values + other['pixel_values'] + else: + pixel_values = pixel_values + torch.tensor(other) + output = ARTInput(**self) + output['pixel_values'] = pixel_values + return output + + def __sub__(self, other): + if isinstance(other, ARTInput): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + pixel_values = pixel_values - other['pixel_values'] + output = ARTInput(**self) + output['pixel_values'] = pixel_values + else: + raise ValueError('Unsupported type for __sub__ in ARTInput') + return output + + def reshape(self, new_shape): + pixel_values = UserDict.__getitem__(self, 'pixel_values') + if not isinstance(pixel_values, torch.Tensor): + pixel_values = torch.tensor(pixel_values) + output = ARTInput(**self) + output['pixel_values'] = torch.reshape(pixel_values, new_shape) + return output diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/estimators/hf_mm/huggingface_mm.py new file mode 100644 index 0000000000..828f9ad074 --- /dev/null +++ b/art/estimators/hf_mm/huggingface_mm.py @@ -0,0 +1,312 @@ +# MIT License +# +# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2023 +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the +# Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +This module implements ... +""" +import logging +from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np + +from art.estimators.pytorch import PyTorchEstimator + + +if TYPE_CHECKING: + # pylint: disable=C0412 + import torch + + from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE + from art.defences.preprocessor.preprocessor import Preprocessor + from art.defences.postprocessor.postprocessor import Postprocessor + +logger = logging.getLogger(__name__) + + +class HFMMPyTorch(PyTorchEstimator): + """ + This module implements ... + """ + + estimator_params = PyTorchEstimator.estimator_params + ["input_shape", "optimizer", "attack_losses"] + + def __init__( + self, + model: "torch.nn.Module", + loss: "torch.nn.modules.loss._Loss", + input_shape: Tuple[int, ...] = (3, 416, 416), + optimizer: Optional["torch.optim.Optimizer"] = None, + clip_values: Optional["CLIP_VALUES_TYPE"] = None, + channels_first: Optional[bool] = True, + preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None, + postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None, + preprocessing: "PREPROCESSING_TYPE" = None, + device_type: str = "gpu", + ): + """ + Initialization. + + :param model: + :param input_shape: The shape of one input sample. + :param optimizer: The optimizer for training the classifier. + :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and + maximum values allowed for features. If floats are provided, these will be used as the range of all + features. If arrays are provided, each value will be considered the bound for a feature, thus + the shape of clip values needs to match the total number of features. + :param channels_first: Set channels first or last. + :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier. + :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier. + :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be + used for data preprocessing. The first value will be subtracted from the input. The input will then + be divided by the second one. + :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU + if available otherwise run on CPU. + """ + import torch + + super().__init__( + model=model, + clip_values=clip_values, + channels_first=channels_first, + preprocessing_defences=preprocessing_defences, + postprocessing_defences=postprocessing_defences, + preprocessing=preprocessing, + device_type=device_type, + ) + + self._input_shape = input_shape + self._optimizer = optimizer + self.loss_fn = loss + + if self.postprocessing_defences is not None: + raise ValueError("This estimator does not support `postprocessing_defences`.") + self._model = model + self._model: torch.nn.Module + self._model.to(self._device) + self._model.eval() + + @property + def model(self) -> "torch.nn.Module": + """ + Return the model. + + :return: The model. + """ + return self._model + + @property + def input_shape(self) -> Tuple[int, ...]: + """ + Return the shape of one input sample. + + :return: Shape of one input sample. + """ + return self._input_shape + + @property + def optimizer(self) -> Optional["torch.optim.Optimizer"]: + """ + Return the optimizer. + + :return: The optimizer. + """ + return self._optimizer + + + @property + def device(self) -> "torch.device": + """ + Get current used device. + + :return: Current used device. + """ + return self._device + + def _preprocess_and_convert_inputs( + self, + x: Union[np.ndarray, "torch.Tensor"], + y: Optional[List[Dict[str, Union[np.ndarray, "torch.Tensor"]]]] = None, + fit: bool = False, + no_grad: bool = True, + ) -> Tuple["torch.Tensor", List[Dict[str, "torch.Tensor"]]]: + """ + Dummy function to allow compatibility with ART attacks. + All pre-processing should be done before by the relevant HF pre-processor. + + :param x: + :param y: + :param fit: `True` if the function is call before fit/training and `False` if the function is called before a + predict operation. + :param no_grad: `True` if no gradients required. + :return: Preprocessed inputs `(x, y)` as tensors. + """ + return x, y + + def _get_losses( + self, x: Dict, y: Union[np.ndarray, "torch.Tensor"] + ) -> Tuple[Dict[str, "torch.Tensor"], "torch.Tensor"]: + """ + Get the loss tensor output of the model including all preprocessing. + + :param x: + :param y: + :return: Loss components and gradients of the input `x`. + """ + self._model.train() + print('x is ', x) + + # Set gradients again after inputs are moved to another device + if x['pixel_values'].is_leaf: + x['pixel_values'].requires_grad = True + else: + x['pixel_values'].retain_grad() + + # Calculate loss components + preds = self._model(**x) + preds = preds.logits_per_image + return self.loss_fn(preds, y) + + def loss_gradient( # pylint: disable=W0613 + self, x: Union[np.ndarray, "torch.Tensor"], y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]], **kwargs + ) -> Union[np.ndarray, "torch.Tensor"]: + """ + Compute the gradient of the loss function w.r.t. `x`. + + :param x: Samples of shape NCHW or NHWC. + :param y: + :return: Loss gradients of the same shape as `x`. + """ + import torch + + loss = self._get_losses(x=x, y=y) + + # Clean gradients + self._model.zero_grad() + + # Compute gradients + loss.backward() # type: ignore + + ''' + if x_grad.grad is not None: + if isinstance(x, np.ndarray): + grads = x_grad.grad.cpu().numpy() + else: + grads = x_grad.grad.clone() + else: + raise ValueError("Gradient term in PyTorch model is `None`.") + ''' + grads = x['pixel_values'].grad + if self.clip_values is not None: + grads = grads / self.clip_values[1] + + if not self.channels_first: + if isinstance(x, np.ndarray): + grads = np.transpose(grads, (0, 2, 3, 1)) + else: + grads = torch.permute(grads, (0, 2, 3, 1)) + # print('loss_gradient: ', x['pixel_values']) + assert grads.shape == x['pixel_values'].shape + return grads.cpu().numpy() + + def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> List[Dict[str, np.ndarray]]: + """ + Perform prediction for a batch of inputs. + + :param x: + :param batch_size: Batch size. + :return: + """ + + # Set model to evaluation mode + self._model.eval() + x_preprocessed, _ = self._preprocess_and_convert_inputs(x=x, y=None, fit=False, no_grad=True) + predictions = self._model(**x) + predictions = predictions.logits_per_image + return predictions + + def fit( # pylint: disable=W0221 + self, + x: np.ndarray, + y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]], + batch_size: int = 128, + nb_epochs: int = 10, + drop_last: bool = False, + scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"] = None, + **kwargs, + ) -> None: + """ + Fit the classifier on the training set + """ + raise NotImplementedError + + def get_activations( + self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False + ) -> np.ndarray: + raise NotImplementedError + + def compute_losses( + self, x: Union[np.ndarray, "torch.Tensor"], y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]] + ) -> Dict[str, np.ndarray]: + """ + Compute all loss components. + + :param x: Samples of shape NCHW or NHWC. + :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, one for each input image. + The fields of the Dict are as follows: + + - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H. + - labels [N]: the labels for each image. + :return: Dictionary of loss components. + """ + loss_components, _ = self._get_losses(x=x, y=y) + output = {} + for key, value in loss_components.items(): + output[key] = value.detach().cpu().numpy() + return output + + def compute_loss( # type: ignore + self, x: Union[np.ndarray, "torch.Tensor"], y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]], **kwargs + ) -> Union[np.ndarray, "torch.Tensor"]: + """ + Compute the loss of the neural network for samples `x`. + + :param x: Samples of shape NCHW or NHWC. + :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, one for each input image. + The fields of the Dict are as follows: + + - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H. + - labels [N]: the labels for each image. + :return: Loss. + """ + import torch + + loss_components, _ = self._get_losses(x=x, y=y) + + # Compute the gradient and return + loss = None + for loss_name in self.attack_losses: + if loss is None: + loss = loss_components[loss_name] + else: + loss = loss + loss_components[loss_name] + + assert loss is not None + + if isinstance(x, torch.Tensor): + return loss + + return loss.detach().cpu().numpy() diff --git a/art/utils.py b/art/utils.py index b8e13d5fae..52694027d1 100644 --- a/art/utils.py +++ b/art/utils.py @@ -39,6 +39,7 @@ from tqdm.auto import tqdm from art import config +from collections import UserDict if TYPE_CHECKING: import torch @@ -557,10 +558,16 @@ def projection(values: np.ndarray, eps: Union[int, float, np.ndarray], norm_p: U elif norm_p in [np.inf, "inf"]: if isinstance(eps, np.ndarray): - eps = eps * np.ones_like(values) + if isinstance(values_tmp, UserDict): + eps = eps * np.ones_like(values['pixel_values'].cpu().detach().numpy()) + else: + eps = eps * np.ones_like(values) eps = eps.reshape([eps.shape[0], -1]) # type: ignore - values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) + if isinstance(values_tmp, UserDict): + values_tmp['pixel_values'] = np.sign(values_tmp['pixel_values'].cpu().detach().numpy()) * np.minimum(abs(values_tmp['pixel_values'].cpu().detach().numpy()), eps) + else: + values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) else: raise NotImplementedError( diff --git a/clip_dev.py b/clip_dev.py new file mode 100644 index 0000000000..8099fe2aa3 --- /dev/null +++ b/clip_dev.py @@ -0,0 +1,64 @@ +import numpy as np +from art.estimators.hf_mm import HFMMPyTorch +from art.estimators.hf_mm import ARTInput + +from art.attacks.evasion import ProjectedGradientDescentPyTorch, ProjectedGradientDescent + +import torch + +MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) +STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) + + +def norm_bound_eps(eps_bound=None): + if eps_bound is None: + eps_bound = np.asarray([8/255, 8/255, 8/255]) + eps_bound = np.abs(eps_bound / STD) + return eps_bound + + +def attack_clip(): + from PIL import Image + import requests + + from transformers import CLIPProcessor, CLIPModel + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + loss_fn = torch.nn.CrossEntropyLoss() + + art_classifier = HFMMPyTorch(model, + loss=loss_fn, + input_shape=(3, 224, 224)) + + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of a cat", "a photo of a dog"] + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + # make a batch + input_list = [] + for _ in range(10): + input_list.append(image) + + inputs = processor(text=text, images=input_list, return_tensors="pt", + padding=True) + + my_input = ARTInput(**inputs) + check_pixels = my_input['pixel_values'] + print('check_pixels ', check_pixels.shape) + check_slicing = my_input[0:5] + print('check_slicing ', check_slicing['pixel_values'].shape) + check_index = my_input[2] + print(check_index) + + labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) + loss = art_classifier._get_losses(my_input, labels) + grad = art_classifier.loss_gradient(my_input, labels) + + attack = ProjectedGradientDescent(art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1) + x_adv = attack.generate(my_input, labels) + +attack_clip() diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py new file mode 100644 index 0000000000..b8c143a8ae --- /dev/null +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -0,0 +1,265 @@ +import numpy as np +import pytest + +MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) +STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) + + +def norm_bound_eps(eps_bound=None): + if eps_bound is None: + eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) + eps_bound = np.abs(eps_bound / STD) + return eps_bound + + +def get_and_process_input(return_batch=False): + + from PIL import Image + import requests + import torch + from transformers import CLIPProcessor + + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of a cat", "a photo of a dog"] + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + + if return_batch: + input_list = [] + for _ in range(10): + input_list.append(image) + inputs = processor(text=text, images=input_list, return_tensors="pt", + padding=True) + original_image = inputs['pixel_values'][0].clone().cpu().numpy() + labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) + + else: + + inputs = processor(text=text, images=image, return_tensors="pt", + padding=True) + original_image = inputs.pixel_values.clone().cpu().numpy() + labels = torch.tensor(np.asarray([0])) + + return inputs, original_image, labels + + +def test_grad_equivalence(): + + import torch + import numpy as np + + from transformers import CLIPModel + + from art.estimators.hf_mm import HFMMPyTorch, ARTInput + + def grad_art(): + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels = get_and_process_input(return_batch=False) + + my_input = ARTInput(**inputs) + art_classifier = HFMMPyTorch(model, + loss=torch.nn.CrossEntropyLoss(), + input_shape=(3, 224, 224)) + + return art_classifier.loss_gradient(my_input, labels) + + def manual_grad(): + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels = get_and_process_input(return_batch=False) + + inputs.pixel_values.requires_grad_(True) + lossfn = torch.nn.CrossEntropyLoss() + + outputs = model(**inputs) + logits_per_image = outputs.logits_per_image # image-text similarity score + + loss = lossfn(logits_per_image, labels) + loss.backward() + + return inputs.pixel_values.grad + + art = grad_art() + manual = manual_grad() + assert np.allclose(art, manual.cpu().detach().numpy()) + + +@pytest.mark.parametrize("to_batch", [False, True]) +def test_perturbation_equivalence(to_batch): + """ + Test that the result from using ART tools matches that obtained by manual calculation. + """ + import torch + from transformers import CLIPProcessor, CLIPModel + + import numpy as np + from art.estimators.hf_mm import HFMMPyTorch, ARTInput + from art.attacks.evasion import ProjectedGradientDescent, ProjectedGradientDescentNumpy + + def attack_clip(): + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + loss_fn = torch.nn.CrossEntropyLoss() + inputs, original_image, labels = get_and_process_input(return_batch=to_batch) + original_image = inputs.pixel_values.clone().cpu().numpy() + + my_input = ARTInput(**inputs) + art_classifier = HFMMPyTorch(model, + loss=loss_fn, + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224)) + + attack = ProjectedGradientDescentNumpy(art_classifier, + max_iter=2, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1,) + + x_pert = attack._compute_perturbation(my_input, labels, mask=None) + + return x_pert + + def manual_attack(): + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels = get_and_process_input(return_batch=to_batch) + + pixel_values = inputs.pixel_values.requires_grad_(True) + attention_mask = inputs.attention_mask + input_ids = inputs.input_ids + + lossfn = torch.nn.CrossEntropyLoss() + + inputs = {"pixel_values": pixel_values.requires_grad_(True), + "attention_mask": attention_mask, + "input_ids": input_ids} + + outputs = model(**inputs) + + loss = lossfn(outputs.logits_per_image, labels) + loss.backward() + + sign = torch.sign(inputs["pixel_values"].grad) + model.zero_grad() + + return sign.cpu().detach().numpy() + + manual_pert = manual_attack() + x_pert = attack_clip() + + assert np.allclose(x_pert, manual_pert) + + +@pytest.mark.parametrize("to_batch", [False, True]) +def test_equivalence(to_batch): + """ + Test that the result from using ART tools matches that obtained by manual calculation. + """ + import torch + from transformers import CLIPProcessor, CLIPModel + + import numpy as np + from art.estimators.hf_mm import HFMMPyTorch, ARTInput + from art.attacks.evasion import ProjectedGradientDescent + + from matplotlib import pyplot as plt + + def attack_clip(): + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + loss_fn = torch.nn.CrossEntropyLoss() + + inputs, original_image, labels = get_and_process_input(return_batch=to_batch) + original_image = inputs.pixel_values.clone().cpu().numpy() + + my_input = ARTInput(**inputs) + + art_classifier = HFMMPyTorch(model, + loss=loss_fn, + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224)) + clip_min, clip_max = art_classifier.clip_values + print('Min ', clip_min) + print('Max ', clip_max) + + attack = ProjectedGradientDescent(art_classifier, + max_iter=2, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1) + + x_adv = attack.generate(my_input, labels) + x_adv = x_adv[0] + check_vals = torch.reshape(x_adv['pixel_values'], (-1, )) + + for val in check_vals: + if not torch.ge(val, np.min(original_image)): + print(f'Val {val} vs Min {np.min(original_image)}') + + # assert torch.all(torch.ge(check_vals, np.min(original_image))) + # assert torch.all(check_vals <= np.max(original_image)) + + ''' + for i, (channel_std, channel_mean) in enumerate(zip(STD, MEAN)): + x_adv['pixel_values'][i, :, :] = x_adv['pixel_values'][i, :, :] * channel_std + x_adv['pixel_values'][i, :, :] = x_adv['pixel_values'][i, :, :] + channel_mean + + # original_image[:, i, :, :] = original_image[:, i, :, :] * channel_std + # original_image[:, i, :, :] = original_image[:, i, :, :] + channel_mean + pixel_values = x_adv['pixel_values'].cpu().numpy() + pixel_values = np.squeeze(np.transpose(pixel_values, (1, 2, 0))) + # original_image = np.squeeze(np.transpose(original_image, (0, 2, 3, 1))) + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(pixel_values) + plt.show() + # ax2.imshow(original_image) + ''' + return x_adv + + def manual_attack(): + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels = get_and_process_input() + + pixel_values = inputs.pixel_values.requires_grad_(True) + attention_mask = inputs.attention_mask + input_ids = inputs.input_ids + + init_max = torch.max(pixel_values) + init_min = torch.min(pixel_values) + + lossfn = torch.nn.CrossEntropyLoss() + eps = norm_bound_eps() + + mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() + maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + + for i in range(2): + print('On step ', i) + # pixel_values = pixel_values.requires_grad_(True) + inputs = {"pixel_values": pixel_values.requires_grad_(True), + "attention_mask": attention_mask, + "input_ids": input_ids} + + outputs = model(**inputs) + + loss = lossfn(outputs.logits_per_image, labels) + loss.backward() + + with torch.no_grad(): + sign = torch.sign(inputs["pixel_values"].grad) + inputs["pixel_values"] = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) + pixel_values = torch.clamp(inputs["pixel_values"], min=mins, max=maxs) + + model.zero_grad() + + return pixel_values.cpu().detach().numpy() + + manual_adv = manual_attack() + art_adv = attack_clip() + + art_adv = art_adv['pixel_values'] + print(art_adv.shape) + print(manual_adv.shape) + print(art_adv[0:3, 0, 0]) + print(manual_adv[0, 0:3, 0, 0]) + + assert np.allclose(art_adv, manual_adv[0]) From 8bbb18ebd4d5d899a5041680df65a7766bf386c4 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Mon, 18 Sep 2023 19:32:55 +0100 Subject: [PATCH 03/46] fix assignment with torch.no_grad Signed-off-by: GiulioZizzo --- art/estimators/hf_mm/hf_inputs.py | 11 ++++++----- clip_dev.py | 6 ++++++ 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index c2ce2e83f7..028970e3c9 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -34,11 +34,12 @@ def __setitem__(self, key, value): if isinstance(key, int): pixel_values = UserDict.__getitem__(self, 'pixel_values') original_shape = pixel_values.shape - if isinstance(value, ARTInput): - pixel_values[key] = value['pixel_values'] - else: - pixel_values[key] = torch.tensor(value) - self['pixel_values'] = pixel_values + with torch.no_grad(): + if isinstance(value, ARTInput): + pixel_values[key] = value['pixel_values'] + else: + pixel_values[key] = torch.tensor(value) + self['pixel_values'] = pixel_values assert self['pixel_values'].shape == original_shape def __getitem__(self, item): diff --git a/clip_dev.py b/clip_dev.py index 8099fe2aa3..e081083d17 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -54,11 +54,17 @@ def attack_clip(): labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) loss = art_classifier._get_losses(my_input, labels) grad = art_classifier.loss_gradient(my_input, labels) + clean_preds = art_classifier.predict(my_input) + print(clean_preds) attack = ProjectedGradientDescent(art_classifier, max_iter=10, eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), eps_step=np.ones((3, 224, 224)) * 0.1) x_adv = attack.generate(my_input, labels) + adv_preds = art_classifier.predict(x_adv) + print(clean_preds) + print(adv_preds) + attack_clip() From 852229eac92ffe72de9897225e2ce58088ee5d75 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Wed, 20 Sep 2023 12:55:07 +0100 Subject: [PATCH 04/46] fix bug in which x.copy() required a deepcopy for the new hf input type Signed-off-by: GiulioZizzo --- art/attacks/evasion/fast_gradient.py | 4 +- art/estimators/hf_mm/hf_inputs.py | 21 ++- art/estimators/hf_mm/huggingface_mm.py | 1 - art/utils.py | 4 +- clip_dev.py | 35 +++- .../attacks/evasion/test_multimodal_attack.py | 165 +++++++++--------- 6 files changed, 124 insertions(+), 106 deletions(-) diff --git a/art/attacks/evasion/fast_gradient.py b/art/attacks/evasion/fast_gradient.py index 38ee1e1b7e..79fcb344a9 100644 --- a/art/attacks/evasion/fast_gradient.py +++ b/art/attacks/evasion/fast_gradient.py @@ -519,7 +519,9 @@ def _compute( x_adv = np.clip(x_adv, clip_min, clip_max) else: if x.dtype == object: - x_adv = x.copy() + import copy + # x_adv = x.copy() + x_adv = copy.deepcopy(x) else: x_adv = x.astype(ART_NUMPY_DTYPE) diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index 028970e3c9..19c73340bc 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -24,14 +24,14 @@ def __setitem__(self, key, value): super().__setitem__('pixel_values', pixel_values) assert self['pixel_values'].shape == original_shape - if isinstance(key, str): + elif isinstance(key, str): super().__setitem__(key, value) if key == 'pixel_values': pixel_values = UserDict.__getitem__(self, 'pixel_values') self.shape = pixel_values.shape self.ndim = pixel_values.ndim - if isinstance(key, int): + elif isinstance(key, int): pixel_values = UserDict.__getitem__(self, 'pixel_values') original_shape = pixel_values.shape with torch.no_grad(): @@ -39,8 +39,11 @@ def __setitem__(self, key, value): pixel_values[key] = value['pixel_values'] else: pixel_values[key] = torch.tensor(value) - self['pixel_values'] = pixel_values + super().__setitem__('pixel_values', pixel_values) assert self['pixel_values'].shape == original_shape + else: + raise ValueError(f'Unsupported key {key} with type {type(key)}, ' + f'value {value} for __setitem__ in ARTInput') def __getitem__(self, item): if isinstance(item, slice): @@ -55,16 +58,18 @@ def __getitem__(self, item): output = ARTInput(**self) output['pixel_values'] = pixel_values return output - elif item in self.keys(): return UserDict.__getitem__(self, item) + else: + raise ValueError('Unsupported item for __getitem__ in ARTInput') def __add__(self, other): pixel_values = UserDict.__getitem__(self, 'pixel_values') - if isinstance(other, ARTInput): - pixel_values = pixel_values + other['pixel_values'] - else: - pixel_values = pixel_values + torch.tensor(other) + with torch.no_grad(): + if isinstance(other, ARTInput): + pixel_values = pixel_values + other['pixel_values'] + else: + pixel_values = pixel_values + torch.tensor(other) output = ARTInput(**self) output['pixel_values'] = pixel_values return output diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/estimators/hf_mm/huggingface_mm.py index 828f9ad074..529406a245 100644 --- a/art/estimators/hf_mm/huggingface_mm.py +++ b/art/estimators/hf_mm/huggingface_mm.py @@ -167,7 +167,6 @@ def _get_losses( :return: Loss components and gradients of the input `x`. """ self._model.train() - print('x is ', x) # Set gradients again after inputs are moved to another device if x['pixel_values'].is_leaf: diff --git a/art/utils.py b/art/utils.py index 52694027d1..648d2bcf96 100644 --- a/art/utils.py +++ b/art/utils.py @@ -565,7 +565,9 @@ def projection(values: np.ndarray, eps: Union[int, float, np.ndarray], norm_p: U eps = eps.reshape([eps.shape[0], -1]) # type: ignore if isinstance(values_tmp, UserDict): - values_tmp['pixel_values'] = np.sign(values_tmp['pixel_values'].cpu().detach().numpy()) * np.minimum(abs(values_tmp['pixel_values'].cpu().detach().numpy()), eps) + sign = np.sign(values_tmp['pixel_values'].cpu().detach().numpy()) + mag = abs(values_tmp['pixel_values'].cpu().detach().numpy()) + values_tmp['pixel_values'] = sign * np.minimum(mag, eps) else: values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) diff --git a/clip_dev.py b/clip_dev.py index e081083d17..43669d02c6 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -26,9 +26,7 @@ def attack_clip(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() - art_classifier = HFMMPyTorch(model, - loss=loss_fn, - input_shape=(3, 224, 224)) + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") text = ["a photo of a cat", "a photo of a dog"] @@ -42,20 +40,21 @@ def attack_clip(): inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_image = inputs['pixel_values'][0].clone().cpu().detach().numpy() + + art_classifier = HFMMPyTorch(model, + loss=loss_fn, + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224)) my_input = ARTInput(**inputs) - check_pixels = my_input['pixel_values'] - print('check_pixels ', check_pixels.shape) - check_slicing = my_input[0:5] - print('check_slicing ', check_slicing['pixel_values'].shape) - check_index = my_input[2] - print(check_index) labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) loss = art_classifier._get_losses(my_input, labels) grad = art_classifier.loss_gradient(my_input, labels) clean_preds = art_classifier.predict(my_input) print(clean_preds) + print('The max perturbation is', np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) attack = ProjectedGradientDescent(art_classifier, max_iter=10, @@ -63,6 +62,24 @@ def attack_clip(): eps_step=np.ones((3, 224, 224)) * 0.1) x_adv = attack.generate(my_input, labels) adv_preds = art_classifier.predict(x_adv) + + eps = norm_bound_eps() + + np.save('eps_mins.npy', original_image - eps.reshape((1, 3, 1, 1))) + np.save('eps_maxs.npy', original_image + eps.reshape((1, 3, 1, 1))) + np.save('original_image.npy', original_image) + + ''' + eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() + eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + + eps_mins = torch.reshape(eps_mins, (-1,)) + eps_maxs = torch.reshape(eps_maxs, (-1,)) + check_vals = x_adv['pixel_values'] + check_vals = check_vals[0].clone().cpu().detach() + check_vals = torch.reshape(check_vals, (-1,)) + ''' + print(clean_preds) print(adv_preds) diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index b8c143a8ae..542124efc1 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -44,13 +44,11 @@ def get_and_process_input(return_batch=False): return inputs, original_image, labels -def test_grad_equivalence(): +@pytest.mark.parametrize("max_iter", [1, 5]) +def test_grad_equivalence(max_iter): import torch - import numpy as np - from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, ARTInput def grad_art(): @@ -58,11 +56,13 @@ def grad_art(): inputs, original_image, labels = get_and_process_input(return_batch=False) my_input = ARTInput(**inputs) - art_classifier = HFMMPyTorch(model, - loss=torch.nn.CrossEntropyLoss(), - input_shape=(3, 224, 224)) + for _ in range(max_iter): + art_classifier = HFMMPyTorch(model, + loss=torch.nn.CrossEntropyLoss(), + input_shape=(3, 224, 224)) - return art_classifier.loss_gradient(my_input, labels) + loss_grad = art_classifier.loss_gradient(my_input, labels) + return loss_grad def manual_grad(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") @@ -70,12 +70,12 @@ def manual_grad(): inputs.pixel_values.requires_grad_(True) lossfn = torch.nn.CrossEntropyLoss() + for _ in range(max_iter): + outputs = model(**inputs) + logits_per_image = outputs.logits_per_image # image-text similarity score - outputs = model(**inputs) - logits_per_image = outputs.logits_per_image # image-text similarity score - - loss = lossfn(logits_per_image, labels) - loss.backward() + loss = lossfn(logits_per_image, labels) + loss.backward() return inputs.pixel_values.grad @@ -92,7 +92,6 @@ def test_perturbation_equivalence(to_batch): import torch from transformers import CLIPProcessor, CLIPModel - import numpy as np from art.estimators.hf_mm import HFMMPyTorch, ARTInput from art.attacks.evasion import ProjectedGradientDescent, ProjectedGradientDescentNumpy @@ -114,144 +113,138 @@ def attack_clip(): eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), eps_step=np.ones((3, 224, 224)) * 0.1,) - x_pert = attack._compute_perturbation(my_input, labels, mask=None) + perturbation = attack._compute_perturbation(my_input, labels, mask=None) + + if to_batch: + batch_index_2 = 10 + else: + batch_index_2 = 1 + + adv_art_x = attack._apply_perturbation(my_input[0:batch_index_2], perturbation, attack.eps_step) - return x_pert + return perturbation, adv_art_x["pixel_values"].cpu().detach().numpy() def manual_attack(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels = get_and_process_input(return_batch=to_batch) - - pixel_values = inputs.pixel_values.requires_grad_(True) - attention_mask = inputs.attention_mask - input_ids = inputs.input_ids - lossfn = torch.nn.CrossEntropyLoss() - inputs = {"pixel_values": pixel_values.requires_grad_(True), - "attention_mask": attention_mask, - "input_ids": input_ids} + inputs['pixel_values'] = inputs['pixel_values'].requires_grad_(True) outputs = model(**inputs) - loss = lossfn(outputs.logits_per_image, labels) loss.backward() - sign = torch.sign(inputs["pixel_values"].grad) - model.zero_grad() - return sign.cpu().detach().numpy() + init_max = torch.max(inputs["pixel_values"]) + init_min = torch.min(inputs["pixel_values"]) + + eps = norm_bound_eps() - manual_pert = manual_attack() - x_pert = attack_clip() + mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() + maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() - assert np.allclose(x_pert, manual_pert) + inputs["pixel_values"] = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) + pixel_values = torch.clamp(inputs["pixel_values"], min=mins, max=maxs) + return sign.cpu().detach().numpy(), pixel_values.cpu().detach().numpy() -@pytest.mark.parametrize("to_batch", [False, True]) -def test_equivalence(to_batch): + manual_pert, manual_sample = manual_attack() + perturbation, current_x = attack_clip() + + assert np.allclose(perturbation, manual_pert) + assert np.allclose(manual_sample, current_x) + + +@pytest.mark.parametrize("max_iter", [1, 5]) +def test_equivalence(max_iter): """ Test that the result from using ART tools matches that obtained by manual calculation. """ import torch from transformers import CLIPProcessor, CLIPModel - import numpy as np from art.estimators.hf_mm import HFMMPyTorch, ARTInput from art.attacks.evasion import ProjectedGradientDescent - from matplotlib import pyplot as plt - def attack_clip(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() - inputs, original_image, labels = get_and_process_input(return_batch=to_batch) + inputs, original_image, labels = get_and_process_input(return_batch=False) original_image = inputs.pixel_values.clone().cpu().numpy() my_input = ARTInput(**inputs) - + eps = norm_bound_eps() art_classifier = HFMMPyTorch(model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224)) - clip_min, clip_max = art_classifier.clip_values - print('Min ', clip_min) - print('Max ', clip_max) attack = ProjectedGradientDescent(art_classifier, - max_iter=2, + max_iter=max_iter, eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1) + eps_step=np.ones((3, 224, 224)) * 0.1, + targeted=False, + num_random_init=0,) x_adv = attack.generate(my_input, labels) x_adv = x_adv[0] check_vals = torch.reshape(x_adv['pixel_values'], (-1, )) - for val in check_vals: - if not torch.ge(val, np.min(original_image)): - print(f'Val {val} vs Min {np.min(original_image)}') - - # assert torch.all(torch.ge(check_vals, np.min(original_image))) - # assert torch.all(check_vals <= np.max(original_image)) - - ''' - for i, (channel_std, channel_mean) in enumerate(zip(STD, MEAN)): - x_adv['pixel_values'][i, :, :] = x_adv['pixel_values'][i, :, :] * channel_std - x_adv['pixel_values'][i, :, :] = x_adv['pixel_values'][i, :, :] + channel_mean - - # original_image[:, i, :, :] = original_image[:, i, :, :] * channel_std - # original_image[:, i, :, :] = original_image[:, i, :, :] + channel_mean - pixel_values = x_adv['pixel_values'].cpu().numpy() - pixel_values = np.squeeze(np.transpose(pixel_values, (1, 2, 0))) - # original_image = np.squeeze(np.transpose(original_image, (0, 2, 3, 1))) - fig, (ax1, ax2) = plt.subplots(1, 2) - ax1.imshow(pixel_values) - plt.show() - # ax2.imshow(original_image) - ''' - return x_adv + assert torch.all(torch.ge(check_vals, np.min(original_image))) + assert torch.all(torch.le(check_vals, np.max(original_image))) - def manual_attack(): + eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() + eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels = get_and_process_input() + eps_mins = torch.reshape(eps_mins, (-1, )) + eps_maxs = torch.reshape(eps_maxs, (-1, )) - pixel_values = inputs.pixel_values.requires_grad_(True) - attention_mask = inputs.attention_mask - input_ids = inputs.input_ids + assert torch.all(torch.ge(check_vals, eps_mins)) + assert torch.all(torch.le(check_vals, eps_maxs)) - init_max = torch.max(pixel_values) - init_min = torch.min(pixel_values) + return x_adv + + def manual_attack(): lossfn = torch.nn.CrossEntropyLoss() eps = norm_bound_eps() + adv_current = None + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() - maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + for i in range(max_iter): - for i in range(2): print('On step ', i) - # pixel_values = pixel_values.requires_grad_(True) - inputs = {"pixel_values": pixel_values.requires_grad_(True), - "attention_mask": attention_mask, - "input_ids": input_ids} + + inputs, original_image, labels = get_and_process_input() + + eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() + eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + init_max = torch.max(inputs['pixel_values']) + init_min = torch.min(inputs['pixel_values']) + + if adv_current is not None: + inputs['pixel_values'] = torch.tensor(adv_current, requires_grad=True) + else: + inputs['pixel_values'].requires_grad_(True) outputs = model(**inputs) loss = lossfn(outputs.logits_per_image, labels) loss.backward() - with torch.no_grad(): - sign = torch.sign(inputs["pixel_values"].grad) - inputs["pixel_values"] = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) - pixel_values = torch.clamp(inputs["pixel_values"], min=mins, max=maxs) + sign = torch.sign(inputs['pixel_values'].grad) + pixel_values = torch.clamp(inputs['pixel_values'] + sign * 0.1, min=init_min, max=init_max) + pixel_values = torch.clamp(pixel_values, min=eps_mins, max=eps_maxs) model.zero_grad() - return pixel_values.cpu().detach().numpy() + adv_current = pixel_values.cpu().detach().numpy() + + return adv_current manual_adv = manual_attack() art_adv = attack_clip() From 988d3481598fb12e0bcd7d2d7921dc7bf46cb1f8 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Mon, 25 Sep 2023 09:31:10 +0100 Subject: [PATCH 05/46] general updates Signed-off-by: GiulioZizzo --- .github/workflows/ci-style-checks.yml | 1 + art/attacks/evasion/fast_gradient.py | 7 +- art/estimators/hf_mm/__init__.py | 3 + art/estimators/hf_mm/hf_inputs.py | 117 +++++++++++++----- art/estimators/hf_mm/huggingface_mm.py | 64 +++++----- art/utils.py | 10 +- clip_dev.py | 117 ++++++++++++------ clip_initial_attack.py | 21 ++-- .../attacks/evasion/test_multimodal_attack.py | 86 ++++++------- 9 files changed, 261 insertions(+), 165 deletions(-) diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml index c8283c8b9d..6aac12d741 100644 --- a/.github/workflows/ci-style-checks.yml +++ b/.github/workflows/ci-style-checks.yml @@ -16,6 +16,7 @@ on: branches: - main - dev* + - clip_attack # Run scheduled CI flow daily schedule: diff --git a/art/attacks/evasion/fast_gradient.py b/art/attacks/evasion/fast_gradient.py index 79fcb344a9..e9af932348 100644 --- a/art/attacks/evasion/fast_gradient.py +++ b/art/attacks/evasion/fast_gradient.py @@ -24,7 +24,6 @@ from __future__ import absolute_import, division, print_function, unicode_literals import logging -from collections import UserDict from typing import Optional, Union, TYPE_CHECKING import numpy as np @@ -33,6 +32,7 @@ from art.attacks.attack import EvasionAttack from art.estimators.estimator import BaseEstimator, LossGradientsMixin from art.estimators.classification.classifier import ClassifierMixin +from art.estimators.hf_mm import ARTInput from art.utils import ( compute_success, get_labels_np_array, @@ -481,9 +481,9 @@ def _apply_perturbation( if self.estimator.clip_values is not None: clip_min, clip_max = self.estimator.clip_values if x.dtype == object: - if isinstance(x, UserDict): + if isinstance(x, ARTInput): for i_obj in range(x.shape[0]): - x[i_obj] = np.clip(x[i_obj]['pixel_values'].cpu().detach().numpy(), clip_min, clip_max) + x[i_obj] = np.clip(x[i_obj]["pixel_values"].cpu().detach().numpy(), clip_min, clip_max) else: for i_obj in range(x.shape[0]): x[i_obj] = np.clip(x[i_obj], clip_min, clip_max) @@ -520,6 +520,7 @@ def _compute( else: if x.dtype == object: import copy + # x_adv = x.copy() x_adv = copy.deepcopy(x) else: diff --git a/art/estimators/hf_mm/__init__.py b/art/estimators/hf_mm/__init__.py index 8f9068e553..60552fc9b5 100644 --- a/art/estimators/hf_mm/__init__.py +++ b/art/estimators/hf_mm/__init__.py @@ -1,2 +1,5 @@ +""" +Module containing estimators for CLIP. +""" from art.estimators.hf_mm.huggingface_mm import HFMMPyTorch from art.estimators.hf_mm.hf_inputs import ARTInput diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index 19c73340bc..adb3a89e14 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -1,5 +1,27 @@ +# MIT License +# +# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2023 +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the +# Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +This module implements a user-defined dictionary which can support array like functionality to enable compatibility +with ART's tools. +""" from collections import UserDict -import torch + +import numpy as np class ARTInput(UserDict): @@ -9,85 +31,118 @@ class ARTInput(UserDict): some arithmetic operations (addition, subtraction), and python operations such as slicing, reshaping, etc to be performed on the correct components of the HF input. """ + dtype = object shape = (1, 3, 224, 224) ndim = 4 def __setitem__(self, key, value): + import torch + + if not isinstance(value, torch.Tensor): + raise ValueError("Supplied values must be either pytorch tensors or numpy arrays") # print('key ', key) # print('value ', value) if isinstance(key, slice): - pixel_values = UserDict.__getitem__(self, 'pixel_values') + pixel_values = UserDict.__getitem__(self, "pixel_values") original_shape = pixel_values.shape with torch.no_grad(): - pixel_values[key] = value['pixel_values'] - super().__setitem__('pixel_values', pixel_values) - assert self['pixel_values'].shape == original_shape + pixel_values[key] = value["pixel_values"] + super().__setitem__("pixel_values", pixel_values) + assert self["pixel_values"].shape == original_shape elif isinstance(key, str): super().__setitem__(key, value) - if key == 'pixel_values': - pixel_values = UserDict.__getitem__(self, 'pixel_values') + if key == "pixel_values": + pixel_values = UserDict.__getitem__(self, "pixel_values") self.shape = pixel_values.shape self.ndim = pixel_values.ndim elif isinstance(key, int): - pixel_values = UserDict.__getitem__(self, 'pixel_values') + pixel_values = UserDict.__getitem__(self, "pixel_values") original_shape = pixel_values.shape with torch.no_grad(): if isinstance(value, ARTInput): - pixel_values[key] = value['pixel_values'] + pixel_values[key] = value["pixel_values"] else: pixel_values[key] = torch.tensor(value) - super().__setitem__('pixel_values', pixel_values) - assert self['pixel_values'].shape == original_shape + super().__setitem__("pixel_values", pixel_values) + assert self["pixel_values"].shape == original_shape else: - raise ValueError(f'Unsupported key {key} with type {type(key)}, ' - f'value {value} for __setitem__ in ARTInput') + raise ValueError( + f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" + ) def __getitem__(self, item): - if isinstance(item, slice): - pixel_values = UserDict.__getitem__(self, 'pixel_values') + # print('__getitem__ key ', item) + # print('with type ', type(item)) + if isinstance(item, (slice, tuple)): + pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] output = ARTInput(**self) - output['pixel_values'] = pixel_values + output["pixel_values"] = pixel_values return output - elif isinstance(item, int): - pixel_values = UserDict.__getitem__(self, 'pixel_values') + if isinstance(item, int): + pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] output = ARTInput(**self) - output['pixel_values'] = pixel_values + output["pixel_values"] = pixel_values return output - elif item in self.keys(): + if item in self.keys(): return UserDict.__getitem__(self, item) - else: - raise ValueError('Unsupported item for __getitem__ in ARTInput') + raise ValueError("Unsupported item for __getitem__ in ARTInput") def __add__(self, other): - pixel_values = UserDict.__getitem__(self, 'pixel_values') + import torch + + pixel_values = UserDict.__getitem__(self, "pixel_values") with torch.no_grad(): if isinstance(other, ARTInput): - pixel_values = pixel_values + other['pixel_values'] + pixel_values = pixel_values + other["pixel_values"] else: pixel_values = pixel_values + torch.tensor(other) output = ARTInput(**self) - output['pixel_values'] = pixel_values + output["pixel_values"] = pixel_values return output def __sub__(self, other): if isinstance(other, ARTInput): - pixel_values = UserDict.__getitem__(self, 'pixel_values') - pixel_values = pixel_values - other['pixel_values'] + pixel_values = UserDict.__getitem__(self, "pixel_values") + pixel_values = pixel_values - other["pixel_values"] output = ARTInput(**self) - output['pixel_values'] = pixel_values + output["pixel_values"] = pixel_values else: - raise ValueError('Unsupported type for __sub__ in ARTInput') + raise ValueError("Unsupported type for __sub__ in ARTInput") + return output + + def __mul__(self, other): + import torch + + if isinstance(other, ARTInput): + pixel_values = UserDict.__getitem__(self, "pixel_values") + pixel_values = pixel_values * other["pixel_values"] + output = ARTInput(**self) + output["pixel_values"] = pixel_values + elif isinstance(other, np.ndarray): + pixel_values = UserDict.__getitem__(self, "pixel_values") + pixel_values = pixel_values * torch.tensor(other) + output = ARTInput(**self) + output["pixel_values"] = pixel_values + else: + raise ValueError("Unsupported type for __mul__ in ARTInput") return output def reshape(self, new_shape): - pixel_values = UserDict.__getitem__(self, 'pixel_values') + """ + Defines reshaping on the HF input. + :param new_shape: The new shape for the input + :return: A ARTInput instance with the pixel values having their shape updated. + """ + import torch + + pixel_values = UserDict.__getitem__(self, "pixel_values") if not isinstance(pixel_values, torch.Tensor): pixel_values = torch.tensor(pixel_values) output = ARTInput(**self) - output['pixel_values'] = torch.reshape(pixel_values, new_shape) + output["pixel_values"] = torch.reshape(pixel_values, new_shape) return output diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/estimators/hf_mm/huggingface_mm.py index 529406a245..0d3dd03b7e 100644 --- a/art/estimators/hf_mm/huggingface_mm.py +++ b/art/estimators/hf_mm/huggingface_mm.py @@ -42,7 +42,7 @@ class HFMMPyTorch(PyTorchEstimator): This module implements ... """ - estimator_params = PyTorchEstimator.estimator_params + ["input_shape", "optimizer", "attack_losses"] + estimator_params = PyTorchEstimator.estimator_params + ["input_shape", "optimizer"] def __init__( self, @@ -91,7 +91,7 @@ def __init__( self._input_shape = input_shape self._optimizer = optimizer self.loss_fn = loss - + self.nb_classes = 2 if self.postprocessing_defences is not None: raise ValueError("This estimator does not support `postprocessing_defences`.") self._model = model @@ -126,7 +126,6 @@ def optimizer(self) -> Optional["torch.optim.Optimizer"]: """ return self._optimizer - @property def device(self) -> "torch.device": """ @@ -138,11 +137,11 @@ def device(self) -> "torch.device": def _preprocess_and_convert_inputs( self, - x: Union[np.ndarray, "torch.Tensor"], - y: Optional[List[Dict[str, Union[np.ndarray, "torch.Tensor"]]]] = None, + x: Dict, + y: Optional[Union[np.ndarray, "torch.Tensor"]] = None, fit: bool = False, no_grad: bool = True, - ) -> Tuple["torch.Tensor", List[Dict[str, "torch.Tensor"]]]: + ) -> Tuple[Dict, Union[np.ndarray, "torch.Tensor", None]]: """ Dummy function to allow compatibility with ART attacks. All pre-processing should be done before by the relevant HF pre-processor. @@ -156,9 +155,7 @@ def _preprocess_and_convert_inputs( """ return x, y - def _get_losses( - self, x: Dict, y: Union[np.ndarray, "torch.Tensor"] - ) -> Tuple[Dict[str, "torch.Tensor"], "torch.Tensor"]: + def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.Tensor": """ Get the loss tensor output of the model including all preprocessing. @@ -166,13 +163,20 @@ def _get_losses( :param y: :return: Loss components and gradients of the input `x`. """ + import torch + self._model.train() + if isinstance(y, np.ndarray): + y = torch.tensor(y) + # reduce labels + if y.ndim > 1: + y = torch.argmax(y, dim=-1) # Set gradients again after inputs are moved to another device - if x['pixel_values'].is_leaf: - x['pixel_values'].requires_grad = True + if x["pixel_values"].is_leaf: + x["pixel_values"].requires_grad = True else: - x['pixel_values'].retain_grad() + x["pixel_values"].retain_grad() # Calculate loss components preds = self._model(**x) @@ -180,7 +184,7 @@ def _get_losses( return self.loss_fn(preds, y) def loss_gradient( # pylint: disable=W0613 - self, x: Union[np.ndarray, "torch.Tensor"], y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]], **kwargs + self, x: Dict, y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> Union[np.ndarray, "torch.Tensor"]: """ Compute the gradient of the loss function w.r.t. `x`. @@ -199,7 +203,7 @@ def loss_gradient( # pylint: disable=W0613 # Compute gradients loss.backward() # type: ignore - ''' + """ if x_grad.grad is not None: if isinstance(x, np.ndarray): grads = x_grad.grad.cpu().numpy() @@ -207,8 +211,8 @@ def loss_gradient( # pylint: disable=W0613 grads = x_grad.grad.clone() else: raise ValueError("Gradient term in PyTorch model is `None`.") - ''' - grads = x['pixel_values'].grad + """ + grads = x["pixel_values"].grad if self.clip_values is not None: grads = grads / self.clip_values[1] @@ -217,11 +221,11 @@ def loss_gradient( # pylint: disable=W0613 grads = np.transpose(grads, (0, 2, 3, 1)) else: grads = torch.permute(grads, (0, 2, 3, 1)) - # print('loss_gradient: ', x['pixel_values']) - assert grads.shape == x['pixel_values'].shape + + assert grads.shape == x["pixel_values"].shape return grads.cpu().numpy() - def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> List[Dict[str, np.ndarray]]: + def predict(self, x: Union[Dict, np.ndarray], batch_size: int = 128, **kwargs) -> np.ndarray: """ Perform prediction for a batch of inputs. @@ -232,10 +236,12 @@ def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> List[Dict[str, np # Set model to evaluation mode self._model.eval() + if isinstance(x, np.ndarray): + raise ValueError('x should be of type art.estimators.hf_mm.hf_inputs.ARTInput') x_preprocessed, _ = self._preprocess_and_convert_inputs(x=x, y=None, fit=False, no_grad=True) - predictions = self._model(**x) + predictions = self._model(**x_preprocessed) predictions = predictions.logits_per_image - return predictions + return predictions.cpu().numpy() def fit( # pylint: disable=W0221 self, @@ -257,9 +263,7 @@ def get_activations( ) -> np.ndarray: raise NotImplementedError - def compute_losses( - self, x: Union[np.ndarray, "torch.Tensor"], y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]] - ) -> Dict[str, np.ndarray]: + def compute_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> Dict: """ Compute all loss components. @@ -278,7 +282,7 @@ def compute_losses( return output def compute_loss( # type: ignore - self, x: Union[np.ndarray, "torch.Tensor"], y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]], **kwargs + self, x: Dict, y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> Union[np.ndarray, "torch.Tensor"]: """ Compute the loss of the neural network for samples `x`. @@ -293,15 +297,7 @@ def compute_loss( # type: ignore """ import torch - loss_components, _ = self._get_losses(x=x, y=y) - - # Compute the gradient and return - loss = None - for loss_name in self.attack_losses: - if loss is None: - loss = loss_components[loss_name] - else: - loss = loss + loss_components[loss_name] + loss, _ = self._get_losses(x=x, y=y) assert loss is not None diff --git a/art/utils.py b/art/utils.py index 648d2bcf96..3504bd23be 100644 --- a/art/utils.py +++ b/art/utils.py @@ -29,6 +29,7 @@ import tarfile import warnings import zipfile +from collections import UserDict from functools import wraps from inspect import signature from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union @@ -39,7 +40,6 @@ from tqdm.auto import tqdm from art import config -from collections import UserDict if TYPE_CHECKING: import torch @@ -559,15 +559,15 @@ def projection(values: np.ndarray, eps: Union[int, float, np.ndarray], norm_p: U elif norm_p in [np.inf, "inf"]: if isinstance(eps, np.ndarray): if isinstance(values_tmp, UserDict): - eps = eps * np.ones_like(values['pixel_values'].cpu().detach().numpy()) + eps = eps * np.ones_like(values["pixel_values"].cpu().detach().numpy()) else: eps = eps * np.ones_like(values) eps = eps.reshape([eps.shape[0], -1]) # type: ignore if isinstance(values_tmp, UserDict): - sign = np.sign(values_tmp['pixel_values'].cpu().detach().numpy()) - mag = abs(values_tmp['pixel_values'].cpu().detach().numpy()) - values_tmp['pixel_values'] = sign * np.minimum(mag, eps) + sign = np.sign(values_tmp["pixel_values"].cpu().detach().numpy()) + mag = abs(values_tmp["pixel_values"].cpu().detach().numpy()) + values_tmp["pixel_values"] = sign * np.minimum(mag, eps) else: values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) diff --git a/clip_dev.py b/clip_dev.py index 43669d02c6..4af1aa1271 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -2,7 +2,7 @@ from art.estimators.hf_mm import HFMMPyTorch from art.estimators.hf_mm import ARTInput -from art.attacks.evasion import ProjectedGradientDescentPyTorch, ProjectedGradientDescent +from art.attacks.evasion import ProjectedGradientDescent import torch @@ -12,12 +12,12 @@ def norm_bound_eps(eps_bound=None): if eps_bound is None: - eps_bound = np.asarray([8/255, 8/255, 8/255]) + eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) eps_bound = np.abs(eps_bound / STD) return eps_bound -def attack_clip(): +def attack_clip_pgd(): from PIL import Image import requests @@ -26,62 +26,109 @@ def attack_clip(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of a cat", "a photo of a dog"] + text = ["a photo of a cat", "a photo of a dog", "a photo of a car"] url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # make a batch input_list = [] + input_text = [] for _ in range(10): input_list.append(image) + input_text.append(text) - inputs = processor(text=text, images=input_list, return_tensors="pt", - padding=True) - original_image = inputs['pixel_values'][0].clone().cpu().detach().numpy() + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - art_classifier = HFMMPyTorch(model, - loss=loss_fn, - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224)) + art_classifier = HFMMPyTorch( + model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) my_input = ARTInput(**inputs) labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) - loss = art_classifier._get_losses(my_input, labels) - grad = art_classifier.loss_gradient(my_input, labels) + # loss = art_classifier._get_losses(my_input, labels) + # grad = art_classifier.loss_gradient(my_input, labels) clean_preds = art_classifier.predict(my_input) - print(clean_preds) - print('The max perturbation is', np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) - - attack = ProjectedGradientDescent(art_classifier, - max_iter=10, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1) + print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) + + attack = ProjectedGradientDescent( + art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) x_adv = attack.generate(my_input, labels) adv_preds = art_classifier.predict(x_adv) eps = norm_bound_eps() - np.save('eps_mins.npy', original_image - eps.reshape((1, 3, 1, 1))) - np.save('eps_maxs.npy', original_image + eps.reshape((1, 3, 1, 1))) - np.save('original_image.npy', original_image) + np.save("eps_mins.npy", original_image - eps.reshape((1, 3, 1, 1))) + np.save("eps_maxs.npy", original_image + eps.reshape((1, 3, 1, 1))) + np.save("original_image.npy", original_image) + + print(clean_preds) + print(adv_preds) + + +def attack_clip_patch(): + + from art.attacks.evasion import AdversarialPatch + + from PIL import Image + import requests + + from transformers import CLIPProcessor, CLIPModel + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + loss_fn = torch.nn.CrossEntropyLoss() + + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of a cat", "a photo of a dog"] + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + # make a batch + input_list = [] + input_text = [] + for _ in range(10): + input_list.append(image) + input_text.append(text) + + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() + + art_classifier = HFMMPyTorch( + model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) + + my_input = ARTInput(**inputs) + + labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) + labels = labels.reshape((-1,)) + # print(labels.shape) + # loss = art_classifier._get_losses(my_input, labels) + # exit() + # grad = art_classifier.loss_gradient(my_input, labels) + clean_preds = art_classifier.predict(my_input) + print(clean_preds) + print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) + + attack = AdversarialPatch(art_classifier, max_iter=10) + x_adv_patch, adv_mask = attack.generate(my_input, labels) + # adv_preds = art_classifier.predict(x_adv) + print(type(x_adv_patch)) + print(x_adv_patch.shape) - ''' - eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() - eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + mod_input = attack.apply_patch(x=my_input, patch_external=x_adv_patch, mask=adv_mask) - eps_mins = torch.reshape(eps_mins, (-1,)) - eps_maxs = torch.reshape(eps_maxs, (-1,)) - check_vals = x_adv['pixel_values'] - check_vals = check_vals[0].clone().cpu().detach() - check_vals = torch.reshape(check_vals, (-1,)) - ''' + adv_preds = art_classifier.predict(mod_input) print(clean_preds) print(adv_preds) -attack_clip() +# attack_clip_pgd() +attack_clip_patch() diff --git a/clip_initial_attack.py b/clip_initial_attack.py index 1c2f656c84..49cabd57f9 100644 --- a/clip_initial_attack.py +++ b/clip_initial_attack.py @@ -6,12 +6,14 @@ MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) + def norm_bound_eps(eps_bound=None): if eps_bound is None: - eps_bound = np.asarray([8/255, 8/255, 8/255]) - eps_bound = np.abs(eps_bound /STD) + eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) + eps_bound = np.abs(eps_bound / STD) return eps_bound + def attack_clip(): from PIL import Image import requests @@ -24,8 +26,7 @@ def attack_clip(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) - inputs = processor(text=text, images=image, return_tensors="pt", - padding=True) + inputs = processor(text=text, images=image, return_tensors="pt", padding=True) original_image = inputs.pixel_values.clone().cpu().numpy() pixel_values = inputs.pixel_values.requires_grad_(True) @@ -43,7 +44,7 @@ def attack_clip(): maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() outputs = model(pixel_values=pixel_values, attention_mask=attention_mask, input_ids=input_ids) - print('Original class: ', text[torch.argmax(outputs.logits_per_image)]) + print("Original class: ", text[torch.argmax(outputs.logits_per_image)]) for i in range(10): pixel_values = pixel_values.requires_grad_(True) @@ -62,15 +63,14 @@ def attack_clip(): model.zero_grad() outputs = model(pixel_values=pixel_values, attention_mask=attention_mask, input_ids=input_ids) - print('Attacked class: ', text[torch.argmax(outputs.logits_per_image)]) + print("Attacked class: ", text[torch.argmax(outputs.logits_per_image)]) for i, (channel_std, channel_mean) in enumerate(zip(STD, MEAN)): pixel_values[:, i, :, :] = pixel_values[:, i, :, :] * channel_std - pixel_values[:, i, :, :] = pixel_values[:, i, :, :] + channel_mean + pixel_values[:, i, :, :] = pixel_values[:, i, :, :] + channel_mean original_image[:, i, :, :] = original_image[:, i, :, :] * channel_std - original_image[:, i, :, :] = original_image[:, i, :, :] + channel_mean - + original_image[:, i, :, :] = original_image[:, i, :, :] + channel_mean pixel_values = pixel_values.cpu().numpy() pixel_values = np.squeeze(np.transpose(pixel_values, (0, 2, 3, 1))) @@ -81,4 +81,5 @@ def attack_clip(): ax2.imshow(original_image) plt.show() -attack_clip() \ No newline at end of file + +attack_clip() diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 542124efc1..a58acbf695 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -29,15 +29,13 @@ def get_and_process_input(return_batch=False): input_list = [] for _ in range(10): input_list.append(image) - inputs = processor(text=text, images=input_list, return_tensors="pt", - padding=True) - original_image = inputs['pixel_values'][0].clone().cpu().numpy() + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().numpy() labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) else: - inputs = processor(text=text, images=image, return_tensors="pt", - padding=True) + inputs = processor(text=text, images=image, return_tensors="pt", padding=True) original_image = inputs.pixel_values.clone().cpu().numpy() labels = torch.tensor(np.asarray([0])) @@ -57,9 +55,7 @@ def grad_art(): my_input = ARTInput(**inputs) for _ in range(max_iter): - art_classifier = HFMMPyTorch(model, - loss=torch.nn.CrossEntropyLoss(), - input_shape=(3, 224, 224)) + art_classifier = HFMMPyTorch(model, loss=torch.nn.CrossEntropyLoss(), input_shape=(3, 224, 224)) loss_grad = art_classifier.loss_gradient(my_input, labels) return loss_grad @@ -90,10 +86,10 @@ def test_perturbation_equivalence(to_batch): Test that the result from using ART tools matches that obtained by manual calculation. """ import torch - from transformers import CLIPProcessor, CLIPModel + from transformers import CLIPModel from art.estimators.hf_mm import HFMMPyTorch, ARTInput - from art.attacks.evasion import ProjectedGradientDescent, ProjectedGradientDescentNumpy + from art.attacks.evasion import ProjectedGradientDescentNumpy def attack_clip(): @@ -103,15 +99,16 @@ def attack_clip(): original_image = inputs.pixel_values.clone().cpu().numpy() my_input = ARTInput(**inputs) - art_classifier = HFMMPyTorch(model, - loss=loss_fn, - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224)) + art_classifier = HFMMPyTorch( + model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) - attack = ProjectedGradientDescentNumpy(art_classifier, - max_iter=2, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1,) + attack = ProjectedGradientDescentNumpy( + art_classifier, + max_iter=2, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) perturbation = attack._compute_perturbation(my_input, labels, mask=None) @@ -130,7 +127,7 @@ def manual_attack(): inputs, original_image, labels = get_and_process_input(return_batch=to_batch) lossfn = torch.nn.CrossEntropyLoss() - inputs['pixel_values'] = inputs['pixel_values'].requires_grad_(True) + inputs["pixel_values"] = inputs["pixel_values"].requires_grad_(True) outputs = model(**inputs) loss = lossfn(outputs.logits_per_image, labels) @@ -163,7 +160,7 @@ def test_equivalence(max_iter): Test that the result from using ART tools matches that obtained by manual calculation. """ import torch - from transformers import CLIPProcessor, CLIPModel + from transformers import CLIPModel from art.estimators.hf_mm import HFMMPyTorch, ARTInput from art.attacks.evasion import ProjectedGradientDescent @@ -178,21 +175,22 @@ def attack_clip(): my_input = ARTInput(**inputs) eps = norm_bound_eps() - art_classifier = HFMMPyTorch(model, - loss=loss_fn, - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224)) - - attack = ProjectedGradientDescent(art_classifier, - max_iter=max_iter, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1, - targeted=False, - num_random_init=0,) + art_classifier = HFMMPyTorch( + model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) + + attack = ProjectedGradientDescent( + art_classifier, + max_iter=max_iter, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + targeted=False, + num_random_init=0, + ) x_adv = attack.generate(my_input, labels) x_adv = x_adv[0] - check_vals = torch.reshape(x_adv['pixel_values'], (-1, )) + check_vals = torch.reshape(x_adv["pixel_values"], (-1,)) assert torch.all(torch.ge(check_vals, np.min(original_image))) assert torch.all(torch.le(check_vals, np.max(original_image))) @@ -200,8 +198,8 @@ def attack_clip(): eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() - eps_mins = torch.reshape(eps_mins, (-1, )) - eps_maxs = torch.reshape(eps_maxs, (-1, )) + eps_mins = torch.reshape(eps_mins, (-1,)) + eps_maxs = torch.reshape(eps_maxs, (-1,)) assert torch.all(torch.ge(check_vals, eps_mins)) assert torch.all(torch.le(check_vals, eps_maxs)) @@ -217,27 +215,25 @@ def manual_attack(): for i in range(max_iter): - print('On step ', i) - inputs, original_image, labels = get_and_process_input() eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() - init_max = torch.max(inputs['pixel_values']) - init_min = torch.min(inputs['pixel_values']) + init_max = torch.max(inputs["pixel_values"]) + init_min = torch.min(inputs["pixel_values"]) if adv_current is not None: - inputs['pixel_values'] = torch.tensor(adv_current, requires_grad=True) + inputs["pixel_values"] = torch.tensor(adv_current, requires_grad=True) else: - inputs['pixel_values'].requires_grad_(True) + inputs["pixel_values"].requires_grad_(True) outputs = model(**inputs) loss = lossfn(outputs.logits_per_image, labels) loss.backward() - sign = torch.sign(inputs['pixel_values'].grad) - pixel_values = torch.clamp(inputs['pixel_values'] + sign * 0.1, min=init_min, max=init_max) + sign = torch.sign(inputs["pixel_values"].grad) + pixel_values = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) pixel_values = torch.clamp(pixel_values, min=eps_mins, max=eps_maxs) model.zero_grad() @@ -249,10 +245,6 @@ def manual_attack(): manual_adv = manual_attack() art_adv = attack_clip() - art_adv = art_adv['pixel_values'] - print(art_adv.shape) - print(manual_adv.shape) - print(art_adv[0:3, 0, 0]) - print(manual_adv[0, 0:3, 0, 0]) + art_adv = art_adv["pixel_values"] assert np.allclose(art_adv, manual_adv[0]) From 5483e81e6fb29a94de23b04724de2bcc6d008322 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 29 Sep 2023 16:57:17 +0100 Subject: [PATCH 06/46] Rename of input, type hinting, function commenting Signed-off-by: GiulioZizzo --- art/attacks/evasion/fast_gradient.py | 4 +- art/estimators/hf_mm/__init__.py | 2 +- art/estimators/hf_mm/hf_inputs.py | 55 ++++-- art/estimators/hf_mm/huggingface_mm.py | 153 +++++++++------ clip_dev.py | 183 ++++++++++++++---- .../attacks/evasion/test_multimodal_attack.py | 89 ++++++--- 6 files changed, 341 insertions(+), 145 deletions(-) diff --git a/art/attacks/evasion/fast_gradient.py b/art/attacks/evasion/fast_gradient.py index e9af932348..8cb9cf1d8f 100644 --- a/art/attacks/evasion/fast_gradient.py +++ b/art/attacks/evasion/fast_gradient.py @@ -32,7 +32,7 @@ from art.attacks.attack import EvasionAttack from art.estimators.estimator import BaseEstimator, LossGradientsMixin from art.estimators.classification.classifier import ClassifierMixin -from art.estimators.hf_mm import ARTInput +from art.estimators.hf_mm import MultiModalHuggingFaceInput from art.utils import ( compute_success, get_labels_np_array, @@ -481,7 +481,7 @@ def _apply_perturbation( if self.estimator.clip_values is not None: clip_min, clip_max = self.estimator.clip_values if x.dtype == object: - if isinstance(x, ARTInput): + if isinstance(x, MultiModalHuggingFaceInput): for i_obj in range(x.shape[0]): x[i_obj] = np.clip(x[i_obj]["pixel_values"].cpu().detach().numpy(), clip_min, clip_max) else: diff --git a/art/estimators/hf_mm/__init__.py b/art/estimators/hf_mm/__init__.py index 60552fc9b5..902b4afea4 100644 --- a/art/estimators/hf_mm/__init__.py +++ b/art/estimators/hf_mm/__init__.py @@ -2,4 +2,4 @@ Module containing estimators for CLIP. """ from art.estimators.hf_mm.huggingface_mm import HFMMPyTorch -from art.estimators.hf_mm.hf_inputs import ARTInput +from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index adb3a89e14..b4604b48ed 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -19,12 +19,15 @@ This module implements a user-defined dictionary which can support array like functionality to enable compatibility with ART's tools. """ +from __future__ import annotations + from collections import UserDict +from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING import numpy as np -class ARTInput(UserDict): +class MultiModalHuggingFaceInput(UserDict): """ Custom class to allow HF inputs which are in a dictionary to be compatible with ART. Allows certain array-like functionality to be performed directly onto the input such as @@ -39,8 +42,9 @@ class ARTInput(UserDict): def __setitem__(self, key, value): import torch - if not isinstance(value, torch.Tensor): - raise ValueError("Supplied values must be either pytorch tensors or numpy arrays") + # if not isinstance(value, (torch.Tensor, np.ndarray, )): + # print(type(value)) + # raise ValueError("Supplied values must be pytorch tensors or numpy arrays") # print('key ', key) # print('value ', value) if isinstance(key, slice): @@ -62,7 +66,7 @@ def __setitem__(self, key, value): pixel_values = UserDict.__getitem__(self, "pixel_values") original_shape = pixel_values.shape with torch.no_grad(): - if isinstance(value, ARTInput): + if isinstance(value, MultiModalHuggingFaceInput): pixel_values[key] = value["pixel_values"] else: pixel_values[key] = torch.tensor(value) @@ -73,66 +77,75 @@ def __setitem__(self, key, value): f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" ) - def __getitem__(self, item): + def __getitem__(self, item: Union[slice, Tuple, int, str]) -> MultiModalHuggingFaceInput: # print('__getitem__ key ', item) # print('with type ', type(item)) if isinstance(item, (slice, tuple)): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] - output = ARTInput(**self) + output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = pixel_values return output if isinstance(item, int): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] - output = ARTInput(**self) + output = MultiModalHuggingFaceInput(**self) + output["pixel_values"] = pixel_values + return output + if isinstance(item, np.ndarray): + pixel_values = UserDict.__getitem__(self, "pixel_values") + pixel_values = pixel_values[item] + output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = pixel_values return output if item in self.keys(): return UserDict.__getitem__(self, item) raise ValueError("Unsupported item for __getitem__ in ARTInput") - def __add__(self, other): + def __add__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> MultiModalHuggingFaceInput: import torch pixel_values = UserDict.__getitem__(self, "pixel_values") with torch.no_grad(): - if isinstance(other, ARTInput): + if isinstance(other, MultiModalHuggingFaceInput): pixel_values = pixel_values + other["pixel_values"] else: pixel_values = pixel_values + torch.tensor(other) - output = ARTInput(**self) + output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = pixel_values return output - def __sub__(self, other): - if isinstance(other, ARTInput): + def __sub__(self, other: MultiModalHuggingFaceInput) -> MultiModalHuggingFaceInput: + if isinstance(other, MultiModalHuggingFaceInput): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values - other["pixel_values"] - output = ARTInput(**self) + output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = pixel_values else: raise ValueError("Unsupported type for __sub__ in ARTInput") return output - def __mul__(self, other): + def __mul__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> MultiModalHuggingFaceInput: import torch - if isinstance(other, ARTInput): + if isinstance(other, MultiModalHuggingFaceInput): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values * other["pixel_values"] - output = ARTInput(**self) - output["pixel_values"] = pixel_values elif isinstance(other, np.ndarray): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values * torch.tensor(other) - output = ARTInput(**self) - output["pixel_values"] = pixel_values else: raise ValueError("Unsupported type for __mul__ in ARTInput") + + output = MultiModalHuggingFaceInput(**self) + output["pixel_values"] = pixel_values return output - def reshape(self, new_shape): + def __len__(self): + pixel_values = UserDict.__getitem__(self, "pixel_values") + return len(pixel_values) + + def reshape(self, new_shape: Tuple) -> MultiModalHuggingFaceInput: """ Defines reshaping on the HF input. :param new_shape: The new shape for the input @@ -143,6 +156,6 @@ def reshape(self, new_shape): pixel_values = UserDict.__getitem__(self, "pixel_values") if not isinstance(pixel_values, torch.Tensor): pixel_values = torch.tensor(pixel_values) - output = ARTInput(**self) + output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = torch.reshape(pixel_values, new_shape) return output diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/estimators/hf_mm/huggingface_mm.py index 0d3dd03b7e..b23463bb57 100644 --- a/art/estimators/hf_mm/huggingface_mm.py +++ b/art/estimators/hf_mm/huggingface_mm.py @@ -19,9 +19,11 @@ This module implements ... """ import logging +import random from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING import numpy as np +from tqdm import tqdm from art.estimators.pytorch import PyTorchEstimator @@ -39,16 +41,18 @@ class HFMMPyTorch(PyTorchEstimator): """ - This module implements ... + This module implements an estimator for attacking pre-trained CLIP by adversarial perturbations on the image. + Currently only supports PGD attacks. """ estimator_params = PyTorchEstimator.estimator_params + ["input_shape", "optimizer"] def __init__( self, - model: "torch.nn.Module", + model: "transformers.PreTrainedModel", loss: "torch.nn.modules.loss._Loss", - input_shape: Tuple[int, ...] = (3, 416, 416), + input_shape: Tuple[int, ...], + nb_classes: int, optimizer: Optional["torch.optim.Optimizer"] = None, clip_values: Optional["CLIP_VALUES_TYPE"] = None, channels_first: Optional[bool] = True, @@ -60,9 +64,10 @@ def __init__( """ Initialization. - :param model: + :param model: CLIP model :param input_shape: The shape of one input sample. :param optimizer: The optimizer for training the classifier. + :param nb_classes: ... :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus @@ -91,7 +96,7 @@ def __init__( self._input_shape = input_shape self._optimizer = optimizer self.loss_fn = loss - self.nb_classes = 2 + self.nb_classes = nb_classes if self.postprocessing_defences is not None: raise ValueError("This estimator does not support `postprocessing_defences`.") self._model = model @@ -135,23 +140,23 @@ def device(self) -> "torch.device": """ return self._device + @staticmethod def _preprocess_and_convert_inputs( - self, x: Dict, y: Optional[Union[np.ndarray, "torch.Tensor"]] = None, - fit: bool = False, - no_grad: bool = True, + fit: bool = False, # pylint: disable=W0613 + no_grad: bool = True, # pylint: disable=W0613 ) -> Tuple[Dict, Union[np.ndarray, "torch.Tensor", None]]: """ Dummy function to allow compatibility with ART attacks. All pre-processing should be done before by the relevant HF pre-processor. - :param x: - :param y: + :param x: Dictionary inputs for the CLIP model. + :param y: Labels :param fit: `True` if the function is call before fit/training and `False` if the function is called before a predict operation. :param no_grad: `True` if no gradients required. - :return: Preprocessed inputs `(x, y)` as tensors. + :return: Preprocessed inputs `(x, y) """ return x, y @@ -159,13 +164,14 @@ def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.T """ Get the loss tensor output of the model including all preprocessing. - :param x: - :param y: + :param x: Dictionary inputs for the CLIP model. + :param y: Labels for the loss :return: Loss components and gradients of the input `x`. """ import torch - self._model.train() + self._model.eval() + if isinstance(y, np.ndarray): y = torch.tensor(y) @@ -185,12 +191,12 @@ def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.T def loss_gradient( # pylint: disable=W0613 self, x: Dict, y: Union[np.ndarray, "torch.Tensor"], **kwargs - ) -> Union[np.ndarray, "torch.Tensor"]: + ) -> np.ndarray: """ - Compute the gradient of the loss function w.r.t. `x`. + Compute the gradient of the loss function w.r.t. the image component of the input - :param x: Samples of shape NCHW or NHWC. - :param y: + :param x: Dictionary inputs for the CLIP model. + :param y: Labels for the loss :return: Loss gradients of the same shape as `x`. """ import torch @@ -202,17 +208,13 @@ def loss_gradient( # pylint: disable=W0613 # Compute gradients loss.backward() # type: ignore + x_grad = x["pixel_values"].grad - """ - if x_grad.grad is not None: - if isinstance(x, np.ndarray): - grads = x_grad.grad.cpu().numpy() - else: - grads = x_grad.grad.clone() + if x_grad is not None: + grads = x_grad.clone() else: raise ValueError("Gradient term in PyTorch model is `None`.") - """ - grads = x["pixel_values"].grad + if self.clip_values is not None: grads = grads / self.clip_values[1] @@ -225,11 +227,11 @@ def loss_gradient( # pylint: disable=W0613 assert grads.shape == x["pixel_values"].shape return grads.cpu().numpy() - def predict(self, x: Union[Dict, np.ndarray], batch_size: int = 128, **kwargs) -> np.ndarray: + def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> np.ndarray: """ Perform prediction for a batch of inputs. - :param x: + :param x: Dictionary inputs for the CLIP model. :param batch_size: Batch size. :return: """ @@ -237,57 +239,100 @@ def predict(self, x: Union[Dict, np.ndarray], batch_size: int = 128, **kwargs) - # Set model to evaluation mode self._model.eval() if isinstance(x, np.ndarray): - raise ValueError('x should be of type art.estimators.hf_mm.hf_inputs.ARTInput') + raise ValueError("x should be of type art.estimators.hf_mm.hf_inputs.MultiModalHuggingFaceInput") x_preprocessed, _ = self._preprocess_and_convert_inputs(x=x, y=None, fit=False, no_grad=True) - predictions = self._model(**x_preprocessed) - predictions = predictions.logits_per_image - return predictions.cpu().numpy() + + num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size))) + results = [] + for m in tqdm(range(num_batch)): + print(type(x)) + x_batch = x[batch_size * m: batch_size * (m + 1)] + print(type(x_batch)) + + predictions = self._model(**x_batch) + results.append(predictions.logits_per_image.cpu().detach().numpy()) + + return np.concatenate(results) def fit( # pylint: disable=W0221 self, - x: np.ndarray, - y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]], + x: Dict, + y: Union[np.ndarray, "torch.Tensor"], batch_size: int = 128, nb_epochs: int = 10, drop_last: bool = False, scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"] = None, + verbose: bool = True, **kwargs, ) -> None: """ Fit the classifier on the training set """ - raise NotImplementedError + import torch + self._model.train() + + # y_preprocessed = self.reduce_labels(y) + + y_tensor = torch.from_numpy(y) + + num_batch = int(np.ceil(len(y_tensor) / float(batch_size))) + ind = np.arange(len(y_tensor)) + + # Start training + for _ in tqdm(range(nb_epochs)): + # Shuffle the examples + random.shuffle(ind) + + # Train for one epoch + pbar = tqdm(range(num_batch), disable=not verbose) + acc = [] + losses = [] + + for m in pbar: + x_batch = x[ind[batch_size * m: batch_size * (m + 1)]] + y_batch = y_tensor[ind[batch_size * m: batch_size * (m + 1)]] + + # Zero the parameter gradients + self._optimizer.zero_grad() + + # Perform prediction + try: + model_outputs = self._model(**x_batch) + except ValueError as err: + if "Expected more than 1 value per channel when training" in str(err): + logger.exception( + "Try dropping the last incomplete batch by setting drop_last=True in " + "method PyTorchClassifier.fit." + ) + raise err + + loss = self.loss_fn(model_outputs["logits_per_image"], y_batch) + + loss.backward() + + self._optimizer.step() + losses.append(loss) + if verbose: + pbar.set_description( + f"Loss {torch.mean(torch.stack(losses)):.2f} " + # f"Acc {np.mean(non_cert_acc):.2f} Cert Acc {np.mean(cert_acc):.2f} " + ) + + if scheduler is not None: + scheduler.step() def get_activations( self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False ) -> np.ndarray: raise NotImplementedError - def compute_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> Dict: - """ - Compute all loss components. - - :param x: Samples of shape NCHW or NHWC. - :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, one for each input image. - The fields of the Dict are as follows: - - - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H. - - labels [N]: the labels for each image. - :return: Dictionary of loss components. - """ - loss_components, _ = self._get_losses(x=x, y=y) - output = {} - for key, value in loss_components.items(): - output[key] = value.detach().cpu().numpy() - return output - def compute_loss( # type: ignore self, x: Dict, y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> Union[np.ndarray, "torch.Tensor"]: """ Compute the loss of the neural network for samples `x`. - :param x: Samples of shape NCHW or NHWC. + :param x: Dictionary inputs for the CLIP model. :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, one for each input image. The fields of the Dict are as follows: diff --git a/clip_dev.py b/clip_dev.py index 4af1aa1271..1acfe92cf0 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,15 +1,56 @@ import numpy as np from art.estimators.hf_mm import HFMMPyTorch -from art.estimators.hf_mm import ARTInput +from art.estimators.hf_mm import MultiModalHuggingFaceInput +import matplotlib.pyplot as plt from art.attacks.evasion import ProjectedGradientDescent import torch +from torchvision import datasets + +import ssl + +ssl._create_default_https_context = ssl._create_unverified_context MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) +def get_and_process_input(to_one_hot=False, return_batch=False): + + from PIL import Image + import requests + import torch + from transformers import CLIPProcessor + + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of a cat", "a photo of a dog", "a photo of a bear"] + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + + if return_batch: + input_list = [] + for _ in range(10): + input_list.append(image) + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().numpy() + if to_one_hot: + labels = np.zeros((10, 3)) + labels = labels[0:10] + 1 + else: + labels = np.zeros((10,)) + + labels = torch.tensor(labels).type(torch.LongTensor) + + else: + + inputs = processor(text=text, images=image, return_tensors="pt", padding=True) + original_image = inputs.pixel_values.clone().cpu().numpy() + labels = torch.tensor(np.asarray([0])) + + return inputs, original_image, labels, len(text) + def norm_bound_eps(eps_bound=None): if eps_bound is None: eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) @@ -17,6 +58,25 @@ def norm_bound_eps(eps_bound=None): return eps_bound +def get_cifar_data(): + train_set = datasets.CIFAR10('./data', train=True, download=True) + test_set = datasets.CIFAR10('./data', train=False, download=True) + + x_train = train_set.data.astype(np.float32) + y_train = np.asarray(train_set.targets) + + x_test = test_set.data.astype(np.float32) + y_test = np.asarray(test_set.targets) + + x_train = np.moveaxis(x_train, [3], [1]) + x_test = np.moveaxis(x_test, [3], [1]) + + x_train = x_train / 255.0 + x_test = x_test / 255.0 + + return (x_train[0:100], y_train[0:100]), (x_test[0:100], y_test[0:100]) + + def attack_clip_pgd(): from PIL import Image import requests @@ -39,13 +99,14 @@ def attack_clip_pgd(): input_text.append(text) inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() art_classifier = HFMMPyTorch( model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) - my_input = ARTInput(**inputs) + my_input = MultiModalHuggingFaceInput(**inputs) labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) # loss = art_classifier._get_losses(my_input, labels) @@ -72,63 +133,107 @@ def attack_clip_pgd(): print(adv_preds) -def attack_clip_patch(): - - from art.attacks.evasion import AdversarialPatch - +def cifar_clip_pgd(): from PIL import Image import requests from transformers import CLIPProcessor, CLIPModel + image_list = ['000000039769.jpg', + '000000000285.jpg', + '000000002006.jpg', + '000000002149.jpg', + '000000005992.jpg', + '000000011615.jpg', + '000000013597.jpg'] + text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - loss_fn = torch.nn.CrossEntropyLoss() - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of a cat", "a photo of a dog"] + labels = torch.tensor(np.asarray([0, 1, 3])) - url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open(requests.get(url, stream=True).raw) - # make a batch input_list = [] - input_text = [] - for _ in range(10): - input_list.append(image) - input_text.append(text) + for fname in ['000000039769.jpg', '000000000285.jpg', '000000002006.jpg', '000000002149.jpg']: + url = 'http://images.cocodataset.org/val2017/' + fname + input_list.append(Image.open(requests.get(url, stream=True).raw)) + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + + loss_fn = torch.nn.CrossEntropyLoss() inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_images = [] + for i in range(3): + original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) + + original_images = np.concatenate(original_images) - original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() art_classifier = HFMMPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), input_shape=(3, 224, 224) ) - my_input = ARTInput(**inputs) - - labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) - labels = labels.reshape((-1,)) - # print(labels.shape) - # loss = art_classifier._get_losses(my_input, labels) - # exit() - # grad = art_classifier.loss_gradient(my_input, labels) + my_input = MultiModalHuggingFaceInput(**inputs) clean_preds = art_classifier.predict(my_input) print(clean_preds) - print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) - attack = AdversarialPatch(art_classifier, max_iter=10) - x_adv_patch, adv_mask = attack.generate(my_input, labels) - # adv_preds = art_classifier.predict(x_adv) - print(type(x_adv_patch)) - print(x_adv_patch.shape) - - mod_input = attack.apply_patch(x=my_input, patch_external=x_adv_patch, mask=adv_mask) - - adv_preds = art_classifier.predict(mod_input) + attack = ProjectedGradientDescent( + art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) + x_adv = attack.generate(my_input, labels) + adv_preds = art_classifier.predict(x_adv) print(clean_preds) print(adv_preds) +def test_fit(): + from transformers import CLIPProcessor, CLIPModel + + (x_train, y_train), (x_test, y_test) = get_cifar_data() + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + + labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] + inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() + + inputs = MultiModalHuggingFaceInput(**inputs) + optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + art_classifier = HFMMPyTorch( + model, + optimizer=optimizer, + nb_classes=10, + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), + np.max(original_image)), + input_shape=(3, 224, 224) + ) + + num_of_samples = len(inputs) + print(num_of_samples) + art_classifier.fit(inputs, y_train) + + +def test_predict(): + import torch + from transformers import CLIPModel + from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels, num_classes = get_and_process_input() + + art_classifier = HFMMPyTorch( + model, + nb_classes=num_classes, + loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) + inputs = MultiModalHuggingFaceInput(**inputs) + + preds = art_classifier.predict(inputs) + print('Pred shape is ', preds.shape) +test_predict() +# test_fit() # attack_clip_pgd() -attack_clip_patch() +# cifar_clip_pgd() diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index a58acbf695..0bcce45509 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -12,7 +12,7 @@ def norm_bound_eps(eps_bound=None): return eps_bound -def get_and_process_input(return_batch=False): +def get_and_process_input(to_one_hot=False, return_batch=False): from PIL import Image import requests @@ -20,7 +20,7 @@ def get_and_process_input(return_batch=False): from transformers import CLIPProcessor processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of a cat", "a photo of a dog"] + text = ["a photo of a cat", "a photo of a dog", "a photo of a bear"] url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) @@ -31,7 +31,13 @@ def get_and_process_input(return_batch=False): input_list.append(image) inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) original_image = inputs["pixel_values"][0].clone().cpu().numpy() - labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) + if to_one_hot: + labels = np.zeros((10, 3)) + labels = labels[0:10] + 1 + else: + labels = np.zeros((10,)) + + labels = torch.tensor(labels).type(torch.LongTensor) else: @@ -39,30 +45,33 @@ def get_and_process_input(return_batch=False): original_image = inputs.pixel_values.clone().cpu().numpy() labels = torch.tensor(np.asarray([0])) - return inputs, original_image, labels + return inputs, original_image, labels, len(text) +@pytest.mark.only_with_platform("huggingface") @pytest.mark.parametrize("max_iter", [1, 5]) def test_grad_equivalence(max_iter): import torch from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, ARTInput + from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput def grad_art(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels = get_and_process_input(return_batch=False) + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) - my_input = ARTInput(**inputs) + my_input = MultiModalHuggingFaceInput(**inputs) for _ in range(max_iter): - art_classifier = HFMMPyTorch(model, loss=torch.nn.CrossEntropyLoss(), input_shape=(3, 224, 224)) - + art_classifier = HFMMPyTorch(model, + nb_classes=num_classes, + loss=torch.nn.CrossEntropyLoss(), + input_shape=(3, 224, 224)) loss_grad = art_classifier.loss_gradient(my_input, labels) return loss_grad def manual_grad(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels = get_and_process_input(return_batch=False) + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) inputs.pixel_values.requires_grad_(True) lossfn = torch.nn.CrossEntropyLoss() @@ -80,27 +89,30 @@ def manual_grad(): assert np.allclose(art, manual.cpu().detach().numpy()) +@pytest.mark.only_with_platform("huggingface") @pytest.mark.parametrize("to_batch", [False, True]) def test_perturbation_equivalence(to_batch): """ - Test that the result from using ART tools matches that obtained by manual calculation. + Test that the perturbation from using ART tools matches that obtained by manual calculation. """ import torch from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, ARTInput + from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput from art.attacks.evasion import ProjectedGradientDescentNumpy def attack_clip(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() - inputs, original_image, labels = get_and_process_input(return_batch=to_batch) + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) original_image = inputs.pixel_values.clone().cpu().numpy() - my_input = ARTInput(**inputs) + my_input = MultiModalHuggingFaceInput(**inputs) art_classifier = HFMMPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + model, + nb_classes=num_classes, + loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) attack = ProjectedGradientDescentNumpy( @@ -112,19 +124,14 @@ def attack_clip(): perturbation = attack._compute_perturbation(my_input, labels, mask=None) - if to_batch: - batch_index_2 = 10 - else: - batch_index_2 = 1 - - adv_art_x = attack._apply_perturbation(my_input[0:batch_index_2], perturbation, attack.eps_step) + adv_art_x = attack._apply_perturbation(my_input[0:], perturbation, attack.eps_step) return perturbation, adv_art_x["pixel_values"].cpu().detach().numpy() def manual_attack(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels = get_and_process_input(return_batch=to_batch) + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) lossfn = torch.nn.CrossEntropyLoss() inputs["pixel_values"] = inputs["pixel_values"].requires_grad_(True) @@ -154,15 +161,17 @@ def manual_attack(): assert np.allclose(manual_sample, current_x) +@pytest.mark.only_with_platform("huggingface") @pytest.mark.parametrize("max_iter", [1, 5]) -def test_equivalence(max_iter): +@pytest.mark.parametrize("to_one_hot", [True, False]) +def test_equivalence(max_iter, to_one_hot): """ Test that the result from using ART tools matches that obtained by manual calculation. """ import torch from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, ARTInput + from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput from art.attacks.evasion import ProjectedGradientDescent def attack_clip(): @@ -170,13 +179,15 @@ def attack_clip(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() - inputs, original_image, labels = get_and_process_input(return_batch=False) + inputs, original_image, labels, num_classes = get_and_process_input(to_one_hot=to_one_hot, return_batch=False) original_image = inputs.pixel_values.clone().cpu().numpy() - my_input = ARTInput(**inputs) + my_input = MultiModalHuggingFaceInput(**inputs) eps = norm_bound_eps() art_classifier = HFMMPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + model, + nb_classes=num_classes, + loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) attack = ProjectedGradientDescent( @@ -215,7 +226,7 @@ def manual_attack(): for i in range(max_iter): - inputs, original_image, labels = get_and_process_input() + inputs, original_image, labels, num_classes = get_and_process_input() eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() @@ -248,3 +259,25 @@ def manual_attack(): art_adv = art_adv["pixel_values"] assert np.allclose(art_adv, manual_adv[0]) + + +""" +TODO: move some of the fits to more appropriate testing files +""" +@pytest.mark.only_with_platform("huggingface") +def test_predict(): + import torch + from transformers import CLIPModel + from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) + + art_classifier = HFMMPyTorch( + model, + nb_classes=num_classes, + loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) + inputs = MultiModalHuggingFaceInput(**inputs) + preds = art_classifier.predict(inputs) + From 699527b27ffde029498589d51843188f47284d34 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 13 Oct 2023 09:13:00 +0100 Subject: [PATCH 07/46] initial adversarial training scripts Signed-off-by: GiulioZizzo --- art/estimators/hf_mm/hf_inputs.py | 4 ++++ clip_dev.py | 35 +++++++++++++++++++++++++++++-- 2 files changed, 37 insertions(+), 2 deletions(-) diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index b4604b48ed..ff02d49a46 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -72,6 +72,10 @@ def __setitem__(self, key, value): pixel_values[key] = torch.tensor(value) super().__setitem__("pixel_values", pixel_values) assert self["pixel_values"].shape == original_shape + elif isinstance(key, np.ndarray): + pixel_values = UserDict.__getitem__(self, "pixel_values") + pixel_values = pixel_values[key] + super().__setitem__("pixel_values", pixel_values) else: raise ValueError( f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" diff --git a/clip_dev.py b/clip_dev.py index 1acfe92cf0..0f65b9497c 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -51,6 +51,7 @@ def get_and_process_input(to_one_hot=False, return_batch=False): return inputs, original_image, labels, len(text) + def norm_bound_eps(eps_bound=None): if eps_bound is None: eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) @@ -165,7 +166,6 @@ def cifar_clip_pgd(): original_images = np.concatenate(original_images) - art_classifier = HFMMPyTorch( model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), input_shape=(3, 224, 224) ) @@ -186,6 +186,7 @@ def cifar_clip_pgd(): print(clean_preds) print(adv_preds) + def test_fit(): from transformers import CLIPProcessor, CLIPModel @@ -233,7 +234,37 @@ def test_predict(): preds = art_classifier.predict(inputs) print('Pred shape is ', preds.shape) -test_predict() + +def test_adv_train(): + import torch + from transformers import CLIPModel + from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput + from art.defences.trainer import AdversarialTrainerMadryPGD + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels, num_classes = get_and_process_input() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + + art_classifier = HFMMPyTorch( + model, + nb_classes=num_classes, + optimizer=optimizer, + loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224) + ) + trainer = AdversarialTrainerMadryPGD(art_classifier, + nb_epochs=10, + eps=8/255, + eps_step=1/255, + max_iter=10, + num_random_init=0) + inputs = MultiModalHuggingFaceInput(**inputs) + + trainer.fit(inputs, labels.detach().cpu().numpy()) + + +test_adv_train() +# test_predict() # test_fit() # attack_clip_pgd() # cifar_clip_pgd() From c4b28a1e8ae505323ba2bfb4fb493b8dcaaa8d63 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 17 Oct 2023 07:04:43 -0500 Subject: [PATCH 08/46] adding initial notebook and cuda compatibility Signed-off-by: GiulioZizzo --- art/estimators/hf_mm/hf_inputs.py | 17 +- art/estimators/hf_mm/huggingface_mm.py | 45 +++- notebooks/clip_attack.ipynb | 236 ++++++++++++++++++ .../attacks/evasion/test_multimodal_attack.py | 64 +++-- 4 files changed, 327 insertions(+), 35 deletions(-) create mode 100644 notebooks/clip_attack.ipynb diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index ff02d49a46..cd7513d28e 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -110,11 +110,19 @@ def __add__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> Multi import torch pixel_values = UserDict.__getitem__(self, "pixel_values") + dev_id = pixel_values.get_device() + with torch.no_grad(): if isinstance(other, MultiModalHuggingFaceInput): - pixel_values = pixel_values + other["pixel_values"] + if dev_id == -1: + pixel_values = pixel_values + other["pixel_values"].to("cpu") + else: + pixel_values = pixel_values + other["pixel_values"].to("cuda:" + str(dev_id)) else: - pixel_values = pixel_values + torch.tensor(other) + if dev_id == -1: + pixel_values = pixel_values + torch.tensor(other) + else: + pixel_values = pixel_values + torch.tensor(other).to("cuda:" + str(dev_id)) output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = pixel_values return output @@ -163,3 +171,8 @@ def reshape(self, new_shape: Tuple) -> MultiModalHuggingFaceInput: output = MultiModalHuggingFaceInput(**self) output["pixel_values"] = torch.reshape(pixel_values, new_shape) return output + + def to(self, device): + for key in self.keys(): + self[key] = self[key].to(device) + return self diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/estimators/hf_mm/huggingface_mm.py index b23463bb57..0883d43254 100644 --- a/art/estimators/hf_mm/huggingface_mm.py +++ b/art/estimators/hf_mm/huggingface_mm.py @@ -104,6 +104,10 @@ def __init__( self._model.to(self._device) self._model.eval() + # Attributes for forward compatibility with progress bar updates. + self.training_loss = [] + self.training_accuracy = [] + @property def model(self) -> "torch.nn.Module": """ @@ -175,6 +179,9 @@ def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.T if isinstance(y, np.ndarray): y = torch.tensor(y) + x = x.to(self.device) + y = y.to(self.device) + # reduce labels if y.ndim > 1: y = torch.argmax(y, dim=-1) @@ -245,9 +252,8 @@ def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> np.ndarray: num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size))) results = [] for m in tqdm(range(num_batch)): - print(type(x)) - x_batch = x[batch_size * m: batch_size * (m + 1)] - print(type(x_batch)) + x_batch = x[batch_size * m : batch_size * (m + 1)] + x_batch = x_batch.to(self.device) predictions = self._model(**x_batch) results.append(predictions.logits_per_image.cpu().detach().numpy()) @@ -269,6 +275,7 @@ def fit( # pylint: disable=W0221 Fit the classifier on the training set """ import torch + self._model.train() # y_preprocessed = self.reduce_labels(y) @@ -281,16 +288,21 @@ def fit( # pylint: disable=W0221 # Start training for _ in tqdm(range(nb_epochs)): # Shuffle the examples - random.shuffle(ind) + # random.shuffle(ind) # Train for one epoch pbar = tqdm(range(num_batch), disable=not verbose) - acc = [] + accs = [] losses = [] for m in pbar: - x_batch = x[ind[batch_size * m: batch_size * (m + 1)]] - y_batch = y_tensor[ind[batch_size * m: batch_size * (m + 1)]] + # x_batch = x[ind[batch_size * m: batch_size * (m + 1)]] + x_batch = x[batch_size * m : batch_size * (m + 1)] + # y_batch = y_tensor[ind[batch_size * m: batch_size * (m + 1)]] + y_batch = y_tensor[batch_size * m : batch_size * (m + 1)].to(self.device) + print("y_batch ", y_batch) + x_batch = x_batch.to(self.device) + assert torch.equal(y_batch, torch.tensor([6, 9]).to(self.device)) # Zero the parameter gradients self._optimizer.zero_grad() @@ -311,12 +323,21 @@ def fit( # pylint: disable=W0221 loss.backward() self._optimizer.step() - losses.append(loss) + losses.append(loss.data.detach().cpu().numpy()) + + if isinstance(y_batch, torch.Tensor): + y_batch = y_batch.detach().cpu().numpy() + + acc = np.sum( + np.argmax(model_outputs["logits_per_image"].detach().cpu().numpy(), axis=1) == y_batch + ) / len(y_batch) + accs.append(acc) + if verbose: - pbar.set_description( - f"Loss {torch.mean(torch.stack(losses)):.2f} " - # f"Acc {np.mean(non_cert_acc):.2f} Cert Acc {np.mean(cert_acc):.2f} " - ) + pbar.set_description(f"Loss {np.mean(np.stack(losses)):.2f} " f"Acc {np.mean(np.stack(accs)):.2f} ") + + self.training_loss.append(losses) + self.training_accuracy.append(accs) if scheduler is not None: scheduler.step() diff --git a/notebooks/clip_attack.ipynb b/notebooks/clip_attack.ipynb new file mode 100644 index 0000000000..b08ce360d1 --- /dev/null +++ b/notebooks/clip_attack.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e111634-8795-4707-b71e-9eb2df0eba78", + "metadata": {}, + "source": [ + "# Attacking CLIP for image classification\n", + "\n", + "In this notebook we show how to use the experimental tools in ART to attack the CLIP model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9da58be9-e228-4928-9f73-d146cbb3cc7a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/giulio.zizzo1/art_clip_17/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import sys\n", + "import numpy as np\n", + "import torch\n", + "\n", + "from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput\n", + "from art.attacks.evasion import ProjectedGradientDescent\n", + "\n", + "\n", + "MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073])\n", + "STD = np.asarray([0.26862954, 0.26130258, 0.27577711])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a9fedf31-54cf-4615-aa30-c731f1b6ec25", + "metadata": {}, + "outputs": [], + "source": [ + "def get_data():\n", + " \"\"\"\n", + " We get sample data from the coco dataset.\n", + " \"\"\"\n", + " from PIL import Image\n", + " import requests\n", + " \n", + " image_list = ['000000039769.jpg',\n", + " '000000000285.jpg',\n", + " '000000002006.jpg',\n", + " '000000002149.jpg']\n", + "\n", + " # Freetext description of the content of the classes we will try and sort the pictures into.\n", + " text = [\"a photo of a cat\", \"a photo of a bear\", \"a photo of a car\", \"a photo of a bus\", \"apples\"]\n", + "\n", + " # Ground truth labels mapping the images into one of the free-text categories. \n", + " # Note, we do not have an image of a car in this sample of data\n", + " labels = torch.tensor(np.asarray([0, 1, 3, 4]))\n", + "\n", + " input_list = []\n", + " for fname in image_list:\n", + " url = 'http://images.cocodataset.org/val2017/' + fname\n", + " input_list.append(Image.open(requests.get(url, stream=True).raw))\n", + "\n", + " return input_list, text, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c361f642-e60f-4aa1-8368-5b320fbc432d", + "metadata": {}, + "outputs": [], + "source": [ + "input_list, text, labels = get_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "baa53f63-eae8-44f0-ad97-df5d67d6119c", + "metadata": {}, + "outputs": [], + "source": [ + "def norm_bound_eps(eps_bound=None):\n", + " \"\"\"\n", + " Helper function to normalise the l_infinity bounds from 0 - 1 into z normalization.\n", + " \"\"\"\n", + " if eps_bound is None:\n", + " eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255])\n", + " eps_bound = np.abs(eps_bound / STD)\n", + " return eps_bound" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "557624e0-3446-4f74-bc8f-383783e49c9d", + "metadata": {}, + "outputs": [], + "source": [ + "def attack(input_list, text, labels):\n", + " \"\"\"\n", + " We now attack the clip model by perturbing the input images using ARTs tools.\n", + " \"\"\"\n", + " from transformers import CLIPProcessor, CLIPModel\n", + "\n", + " model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n", + " processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n", + "\n", + " loss_fn = torch.nn.CrossEntropyLoss()\n", + " inputs = processor(text=text, images=input_list, return_tensors=\"pt\", padding=True)\n", + " original_images = []\n", + " for i in range(3):\n", + " original_images.append(inputs[\"pixel_values\"][i].clone().cpu().detach().numpy())\n", + " original_images = np.concatenate(original_images)\n", + "\n", + " art_classifier = HFMMPyTorch(\n", + " model, \n", + " loss=loss_fn,\n", + " nb_classes=5,\n", + " clip_values=(np.min(original_images), np.max(original_images)), \n", + " input_shape=(3, 224, 224)\n", + " )\n", + "\n", + " art_input = MultiModalHuggingFaceInput(**inputs)\n", + " clean_preds = art_classifier.predict(art_input)\n", + " clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels)\n", + " print('The clean accuracy is ', clean_acc)\n", + "\n", + " attack = ProjectedGradientDescent(\n", + " art_classifier,\n", + " max_iter=10,\n", + " eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)),\n", + " eps_step=np.ones((3, 224, 224)) * 0.1,\n", + " )\n", + " x_adv = attack.generate(art_input, labels)\n", + " adv_preds = art_classifier.predict(x_adv)\n", + " adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels)\n", + "\n", + " print('The adversarial accuracy is ', clean_acc)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5f011a60-2381-4d3f-866a-a39ae2279dde", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-17 06:11:36.199655: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-10-17 06:11:36.232269: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2023-10-17 06:11:36.232299: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2023-10-17 06:11:36.232327: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-10-17 06:11:36.240857: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-10-17 06:11:37.143845: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.38it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The clean accuracy is 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PGD - Iterations: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 10.61it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 39.43it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The adversarial accuracy is 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Running the attack we see the performance drop from 100% to 0%.\n", + "attack(input_list, text, labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8332f36d-531c-4311-b6e2-32bb22d9f835", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 0bcce45509..1c7dd612e4 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -51,8 +51,9 @@ def get_and_process_input(to_one_hot=False, return_batch=False): @pytest.mark.only_with_platform("huggingface") @pytest.mark.parametrize("max_iter", [1, 5]) def test_grad_equivalence(max_iter): - import torch + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from transformers import CLIPModel from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput @@ -62,16 +63,21 @@ def grad_art(): my_input = MultiModalHuggingFaceInput(**inputs) for _ in range(max_iter): - art_classifier = HFMMPyTorch(model, - nb_classes=num_classes, - loss=torch.nn.CrossEntropyLoss(), - input_shape=(3, 224, 224)) + art_classifier = HFMMPyTorch( + model, + nb_classes=num_classes, + loss=torch.nn.CrossEntropyLoss(), + input_shape=(3, 224, 224), + device_type="gpu", + ) loss_grad = art_classifier.loss_gradient(my_input, labels) return loss_grad def manual_grad(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + model = model.to(device) inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) + inputs = inputs.to(device) inputs.pixel_values.requires_grad_(True) lossfn = torch.nn.CrossEntropyLoss() @@ -79,7 +85,7 @@ def manual_grad(): outputs = model(**inputs) logits_per_image = outputs.logits_per_image # image-text similarity score - loss = lossfn(logits_per_image, labels) + loss = lossfn(logits_per_image, labels.to(device)) loss.backward() return inputs.pixel_values.grad @@ -96,6 +102,9 @@ def test_perturbation_equivalence(to_batch): Test that the perturbation from using ART tools matches that obtained by manual calculation. """ import torch + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + from transformers import CLIPModel from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput @@ -112,7 +121,9 @@ def attack_clip(): art_classifier = HFMMPyTorch( model, nb_classes=num_classes, - loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + loss=loss_fn, + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), ) attack = ProjectedGradientDescentNumpy( @@ -131,13 +142,15 @@ def attack_clip(): def manual_attack(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + model = model.to(device) inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) lossfn = torch.nn.CrossEntropyLoss() + inputs = inputs.to(device) inputs["pixel_values"] = inputs["pixel_values"].requires_grad_(True) outputs = model(**inputs) - loss = lossfn(outputs.logits_per_image, labels) + loss = lossfn(outputs.logits_per_image, labels.to(device)) loss.backward() sign = torch.sign(inputs["pixel_values"].grad) @@ -146,8 +159,8 @@ def manual_attack(): eps = norm_bound_eps() - mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() - maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() + mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float().to(device) + maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float().to(device) inputs["pixel_values"] = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) pixel_values = torch.clamp(inputs["pixel_values"], min=mins, max=maxs) @@ -169,6 +182,9 @@ def test_equivalence(max_iter, to_one_hot): Test that the result from using ART tools matches that obtained by manual calculation. """ import torch + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + from transformers import CLIPModel from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput @@ -187,7 +203,9 @@ def attack_clip(): art_classifier = HFMMPyTorch( model, nb_classes=num_classes, - loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + loss=loss_fn, + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), ) attack = ProjectedGradientDescent( @@ -223,24 +241,25 @@ def manual_attack(): eps = norm_bound_eps() adv_current = None model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + model = model.to(device) for i in range(max_iter): inputs, original_image, labels, num_classes = get_and_process_input() + inputs = inputs.to(device) - eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float() - eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float() - init_max = torch.max(inputs["pixel_values"]) - init_min = torch.min(inputs["pixel_values"]) + eps_mins = torch.tensor(original_image - eps.reshape((1, 3, 1, 1))).float().to(device) + eps_maxs = torch.tensor(original_image + eps.reshape((1, 3, 1, 1))).float().to(device) + init_max = torch.max(inputs["pixel_values"]).to(device) + init_min = torch.min(inputs["pixel_values"]).to(device) if adv_current is not None: - inputs["pixel_values"] = torch.tensor(adv_current, requires_grad=True) - else: - inputs["pixel_values"].requires_grad_(True) + inputs["pixel_values"] = torch.tensor(adv_current).to(device) + inputs["pixel_values"].requires_grad_(True) outputs = model(**inputs) - loss = lossfn(outputs.logits_per_image, labels) + loss = lossfn(outputs.logits_per_image, labels.to(device)) loss.backward() sign = torch.sign(inputs["pixel_values"].grad) @@ -264,6 +283,8 @@ def manual_attack(): """ TODO: move some of the fits to more appropriate testing files """ + + @pytest.mark.only_with_platform("huggingface") def test_predict(): import torch @@ -276,8 +297,9 @@ def test_predict(): art_classifier = HFMMPyTorch( model, nb_classes=num_classes, - loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), ) inputs = MultiModalHuggingFaceInput(**inputs) preds = art_classifier.predict(inputs) - From f04f13257e232228ac52138ad4d3adcf780b8134 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 17 Oct 2023 10:05:20 -0500 Subject: [PATCH 09/46] pylint and mypy edits Signed-off-by: GiulioZizzo --- art/estimators/hf_mm/hf_inputs.py | 29 +++++++++-- art/estimators/hf_mm/huggingface_mm.py | 69 ++++++++++++++------------ 2 files changed, 63 insertions(+), 35 deletions(-) diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/estimators/hf_mm/hf_inputs.py index cd7513d28e..9184630565 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/estimators/hf_mm/hf_inputs.py @@ -22,10 +22,14 @@ from __future__ import annotations from collections import UserDict -from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING +from typing import List, Tuple, Union, TYPE_CHECKING import numpy as np +if TYPE_CHECKING: + # pylint: disable=C0412 + import torch + class MultiModalHuggingFaceInput(UserDict): """ @@ -81,7 +85,7 @@ def __setitem__(self, key, value): f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" ) - def __getitem__(self, item: Union[slice, Tuple, int, str]) -> MultiModalHuggingFaceInput: + def __getitem__(self, item: Union[slice, Tuple, int, str]) -> Union[MultiModalHuggingFaceInput, "torch.Tensor"]: # print('__getitem__ key ', item) # print('with type ', type(item)) if isinstance(item, (slice, tuple)): @@ -172,7 +176,26 @@ def reshape(self, new_shape: Tuple) -> MultiModalHuggingFaceInput: output["pixel_values"] = torch.reshape(pixel_values, new_shape) return output - def to(self, device): + def to(self, device: Union["torch.device", str]) -> MultiModalHuggingFaceInput: # pylint: disable=C0103 + """ + Moves tensors to the supplied device + :param device: device to move the tensors to. + :return: A MultiModalHuggingFaceInput instance with tensors moved to the supplied device + """ for key in self.keys(): self[key] = self[key].to(device) return self + + @staticmethod + def is_leaf(): + """ + Enable mypy compatibility + """ + raise ValueError("Trying to acces is_leaf for the whole dictionay. Please use on individual tensors") + + @staticmethod + def grad(): + """ + Enable mypy compatibility + """ + raise ValueError("Trying to acces is_leaf for the whole dictionay. Please use on individual tensors") diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/estimators/hf_mm/huggingface_mm.py index 0883d43254..45f021981a 100644 --- a/art/estimators/hf_mm/huggingface_mm.py +++ b/art/estimators/hf_mm/huggingface_mm.py @@ -20,7 +20,7 @@ """ import logging import random -from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING +from typing import List, Optional, Any, Tuple, Union, TYPE_CHECKING import numpy as np from tqdm import tqdm @@ -31,10 +31,11 @@ if TYPE_CHECKING: # pylint: disable=C0412 import torch - + import transformers from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE from art.defences.preprocessor.preprocessor import Preprocessor from art.defences.postprocessor.postprocessor import Postprocessor + from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput logger = logging.getLogger(__name__) @@ -105,8 +106,8 @@ def __init__( self._model.eval() # Attributes for forward compatibility with progress bar updates. - self.training_loss = [] - self.training_accuracy = [] + self.training_loss: List[Any] = [] + self.training_accuracy: List[Any] = [] @property def model(self) -> "torch.nn.Module": @@ -146,11 +147,11 @@ def device(self) -> "torch.device": @staticmethod def _preprocess_and_convert_inputs( - x: Dict, + x: "MultiModalHuggingFaceInput", y: Optional[Union[np.ndarray, "torch.Tensor"]] = None, fit: bool = False, # pylint: disable=W0613 no_grad: bool = True, # pylint: disable=W0613 - ) -> Tuple[Dict, Union[np.ndarray, "torch.Tensor", None]]: + ) -> Tuple["MultiModalHuggingFaceInput", Union[np.ndarray, "torch.Tensor", None]]: """ Dummy function to allow compatibility with ART attacks. All pre-processing should be done before by the relevant HF pre-processor. @@ -164,7 +165,7 @@ def _preprocess_and_convert_inputs( """ return x, y - def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.Tensor": + def _get_losses(self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "torch.Tensor"]) -> "torch.Tensor": """ Get the loss tensor output of the model including all preprocessing. @@ -179,15 +180,15 @@ def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.T if isinstance(y, np.ndarray): y = torch.tensor(y) - x = x.to(self.device) - y = y.to(self.device) + x = x.to(self._device) + y = y.to(self._device) # reduce labels if y.ndim > 1: y = torch.argmax(y, dim=-1) # Set gradients again after inputs are moved to another device if x["pixel_values"].is_leaf: - x["pixel_values"].requires_grad = True + x["pixel_values"].requires_grad = True # type: ignore else: x["pixel_values"].retain_grad() @@ -197,7 +198,7 @@ def _get_losses(self, x: Dict, y: Union[np.ndarray, "torch.Tensor"]) -> "torch.T return self.loss_fn(preds, y) def loss_gradient( # pylint: disable=W0613 - self, x: Dict, y: Union[np.ndarray, "torch.Tensor"], **kwargs + self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> np.ndarray: """ Compute the gradient of the loss function w.r.t. the image component of the input @@ -218,6 +219,7 @@ def loss_gradient( # pylint: disable=W0613 x_grad = x["pixel_values"].grad if x_grad is not None: + assert isinstance(x_grad, torch.Tensor) grads = x_grad.clone() else: raise ValueError("Gradient term in PyTorch model is `None`.") @@ -234,7 +236,9 @@ def loss_gradient( # pylint: disable=W0613 assert grads.shape == x["pixel_values"].shape return grads.cpu().numpy() - def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> np.ndarray: + def predict( + self, x: Union["MultiModalHuggingFaceInput", np.ndarray], batch_size: int = 128, **kwargs + ) -> np.ndarray: """ Perform prediction for a batch of inputs. @@ -242,6 +246,7 @@ def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> np.ndarray: :param batch_size: Batch size. :return: """ + from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput # Set model to evaluation mode self._model.eval() @@ -253,16 +258,18 @@ def predict(self, x: Dict, batch_size: int = 128, **kwargs) -> np.ndarray: results = [] for m in tqdm(range(num_batch)): x_batch = x[batch_size * m : batch_size * (m + 1)] - x_batch = x_batch.to(self.device) - - predictions = self._model(**x_batch) + x_batch = x_batch.to(self._device) + if isinstance(x_batch, MultiModalHuggingFaceInput): + predictions = self._model(**x_batch) + else: + raise ValueError("expected art.estimators.hf_mm.hf_inputs.MultiModalHuggingFaceInput") results.append(predictions.logits_per_image.cpu().detach().numpy()) return np.concatenate(results) def fit( # pylint: disable=W0221 self, - x: Dict, + x: Union[np.ndarray, "MultiModalHuggingFaceInput"], y: Union[np.ndarray, "torch.Tensor"], batch_size: int = 128, nb_epochs: int = 10, @@ -275,9 +282,11 @@ def fit( # pylint: disable=W0221 Fit the classifier on the training set """ import torch + from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput self._model.train() - + if self._optimizer is None: + raise ValueError("Please supply a optimizer") # y_preprocessed = self.reduce_labels(y) y_tensor = torch.from_numpy(y) @@ -288,7 +297,7 @@ def fit( # pylint: disable=W0221 # Start training for _ in tqdm(range(nb_epochs)): # Shuffle the examples - # random.shuffle(ind) + random.shuffle(ind) # Train for one epoch pbar = tqdm(range(num_batch), disable=not verbose) @@ -296,20 +305,20 @@ def fit( # pylint: disable=W0221 losses = [] for m in pbar: - # x_batch = x[ind[batch_size * m: batch_size * (m + 1)]] - x_batch = x[batch_size * m : batch_size * (m + 1)] - # y_batch = y_tensor[ind[batch_size * m: batch_size * (m + 1)]] - y_batch = y_tensor[batch_size * m : batch_size * (m + 1)].to(self.device) - print("y_batch ", y_batch) - x_batch = x_batch.to(self.device) - assert torch.equal(y_batch, torch.tensor([6, 9]).to(self.device)) + x_batch = x[ind[batch_size * m : batch_size * (m + 1)]] + y_batch = y_tensor[ind[batch_size * m : batch_size * (m + 1)]] + + x_batch = x_batch.to(self._device) # Zero the parameter gradients self._optimizer.zero_grad() # Perform prediction try: - model_outputs = self._model(**x_batch) + if isinstance(x_batch, MultiModalHuggingFaceInput): + model_outputs = self._model(**x_batch) + else: + raise ValueError("expected art.estimators.hf_mm.hf_inputs.MultiModalHuggingFaceInput") except ValueError as err: if "Expected more than 1 value per channel when training" in str(err): logger.exception( @@ -348,17 +357,13 @@ def get_activations( raise NotImplementedError def compute_loss( # type: ignore - self, x: Dict, y: Union[np.ndarray, "torch.Tensor"], **kwargs + self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> Union[np.ndarray, "torch.Tensor"]: """ Compute the loss of the neural network for samples `x`. :param x: Dictionary inputs for the CLIP model. - :param y: Target values of format `List[Dict[str, Union[np.ndarray, torch.Tensor]]]`, one for each input image. - The fields of the Dict are as follows: - - - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H. - - labels [N]: the labels for each image. + :param y: Target values :return: Loss. """ import torch From f75ef033c67d1b8916099f0f4565a16c911d645e Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Sat, 21 Oct 2023 11:28:14 -0500 Subject: [PATCH 10/46] refactor to experimental Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 1 + .github/workflows/ci-pytorch.yml | 1 + art/attacks/evasion/fast_gradient.py | 10 +- .../projected_gradient_descent_numpy.py | 7 +- art/defences/trainer/adversarial_trainer.py | 20 ++- art/estimators/__init__.py | 1 - art/estimators/hf_mm/__init__.py | 5 - art/experimental/estimators/__init__.py | 1 + .../huggingface_multimodal/__init__.py | 5 + .../huggingface_multimodal}/huggingface_mm.py | 36 ++-- .../huggingface_mm_inputs.py} | 38 ++-- art_adv_1.npy | Bin 0 -> 602240 bytes art_adv_5.npy | Bin 0 -> 602240 bytes run_single_test.sh | 27 +++ run_tests.sh | 162 +----------------- .../attacks/evasion/test_multimodal_attack.py | 90 +++++++--- 16 files changed, 167 insertions(+), 237 deletions(-) delete mode 100644 art/estimators/hf_mm/__init__.py create mode 100644 art/experimental/estimators/huggingface_multimodal/__init__.py rename art/{estimators/hf_mm => experimental/estimators/huggingface_multimodal}/huggingface_mm.py (89%) rename art/{estimators/hf_mm/hf_inputs.py => experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py} (85%) create mode 100644 art_adv_1.npy create mode 100644 art_adv_5.npy create mode 100644 run_single_test.sh diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index ed3056ad06..c0737d8524 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -16,6 +16,7 @@ on: branches: - main - dev* + - clip_attack # Run scheduled CI flow daily schedule: diff --git a/.github/workflows/ci-pytorch.yml b/.github/workflows/ci-pytorch.yml index d162dfdcbd..ef231a5b1a 100644 --- a/.github/workflows/ci-pytorch.yml +++ b/.github/workflows/ci-pytorch.yml @@ -16,6 +16,7 @@ on: branches: - main - dev* + - clip_attack # Run scheduled CI flow daily schedule: diff --git a/art/attacks/evasion/fast_gradient.py b/art/attacks/evasion/fast_gradient.py index 8cb9cf1d8f..b42288b130 100644 --- a/art/attacks/evasion/fast_gradient.py +++ b/art/attacks/evasion/fast_gradient.py @@ -23,6 +23,7 @@ """ from __future__ import absolute_import, division, print_function, unicode_literals +import copy import logging from typing import Optional, Union, TYPE_CHECKING @@ -32,7 +33,7 @@ from art.attacks.attack import EvasionAttack from art.estimators.estimator import BaseEstimator, LossGradientsMixin from art.estimators.classification.classifier import ClassifierMixin -from art.estimators.hf_mm import MultiModalHuggingFaceInput +from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput from art.utils import ( compute_success, get_labels_np_array, @@ -136,7 +137,7 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes). :return: An array holding the adversarial examples. """ - adv_x = x.copy() + adv_x = copy.deepcopy(x) # Compute perturbation with implicit batching for batch_id in range(int(np.ceil(adv_x.shape[0] / float(self.batch_size)))): @@ -481,7 +482,7 @@ def _apply_perturbation( if self.estimator.clip_values is not None: clip_min, clip_max = self.estimator.clip_values if x.dtype == object: - if isinstance(x, MultiModalHuggingFaceInput): + if isinstance(x, HuggingFaceMultiModalInput): for i_obj in range(x.shape[0]): x[i_obj] = np.clip(x[i_obj]["pixel_values"].cpu().detach().numpy(), clip_min, clip_max) else: @@ -519,9 +520,6 @@ def _compute( x_adv = np.clip(x_adv, clip_min, clip_max) else: if x.dtype == object: - import copy - - # x_adv = x.copy() x_adv = copy.deepcopy(x) else: x_adv = x.astype(ART_NUMPY_DTYPE) diff --git a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index bcb9c0686e..e6fd6df8d4 100644 --- a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -24,7 +24,7 @@ | Paper link: https://arxiv.org/abs/1706.06083 """ from __future__ import absolute_import, division, print_function, unicode_literals - +import copy import logging from typing import Optional, Union, TYPE_CHECKING @@ -376,7 +376,7 @@ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> n if rand_init_num == 0: # initial (and possibly only) random restart: we only have this set of # adversarial examples for now - adv_x[batch_index_1:batch_index_2] = np.copy(batch) + adv_x[batch_index_1:batch_index_2] = copy.deepcopy(batch) else: # replace adversarial examples if they are successful attack_success = compute_success_array( @@ -410,7 +410,8 @@ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> n # Start to compute adversarial examples if x.dtype == object: - adv_x = x.copy() + # adv_x = x.copy() + adv_x = copy.deepcopy(x) else: adv_x = x.astype(ART_NUMPY_DTYPE) diff --git a/art/defences/trainer/adversarial_trainer.py b/art/defences/trainer/adversarial_trainer.py index 477537d860..a93ffbb2a9 100644 --- a/art/defences/trainer/adversarial_trainer.py +++ b/art/defences/trainer/adversarial_trainer.py @@ -205,7 +205,8 @@ def fit( # pylint: disable=W0221 nb_batches = int(np.ceil(len(x) / batch_size)) ind = np.arange(len(x)) attack_id = 0 - + from tqdm import tqdm + import torch # Precompute adversarial samples for transferred attacks logged = False self._precomputed_adv_samples = [] @@ -222,11 +223,14 @@ def fit( # pylint: disable=W0221 else: self._precomputed_adv_samples.append(None) - for _ in trange(nb_epochs, desc="Adversarial training epochs"): + for epoch in trange(nb_epochs, desc="Adversarial training epochs"): # Shuffle the examples - np.random.shuffle(ind) + # np.random.shuffle(ind) + pbar = tqdm(range(nb_batches)) + self._classifier.training_loss = [] + self._classifier.training_accuracy = [] - for batch_id in range(nb_batches): + for batch_id in pbar: # Create batch data x_batch = x[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]].copy() y_batch = y[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]] @@ -255,8 +259,14 @@ def fit( # pylint: disable=W0221 x_batch[adv_ids] = x_adv # Fit batch - self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=0, **kwargs) + self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=False, **kwargs) + pbar.set_description( + f"Loss {np.mean(np.stack(self._classifier.training_loss)):.2f} " + f"Acc {np.mean(self._classifier.training_accuracy):.2f} " + ) attack_id = (attack_id + 1) % len(self.attacks) + # torch.save(self._classifier.model.state_dict(), 'clip_adv_trained_' + str(epoch) + '.pt') + # torch.save(self._classifier._optimizer.state_dict(), 'clip__opt_adv_trained_' + str(epoch) + '.pt') def predict(self, x: np.ndarray, **kwargs) -> np.ndarray: """ diff --git a/art/estimators/__init__.py b/art/estimators/__init__.py index 190237ecbc..f9a7d41928 100644 --- a/art/estimators/__init__.py +++ b/art/estimators/__init__.py @@ -17,7 +17,6 @@ from art.estimators import certification from art.estimators import classification from art.estimators import encoding -from art.estimators import hf_mm from art.estimators import generation from art.estimators import object_detection from art.estimators import poison_mitigation diff --git a/art/estimators/hf_mm/__init__.py b/art/estimators/hf_mm/__init__.py deleted file mode 100644 index 902b4afea4..0000000000 --- a/art/estimators/hf_mm/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Module containing estimators for CLIP. -""" -from art.estimators.hf_mm.huggingface_mm import HFMMPyTorch -from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput diff --git a/art/experimental/estimators/__init__.py b/art/experimental/estimators/__init__.py index 7016108edb..2cd257e783 100644 --- a/art/experimental/estimators/__init__.py +++ b/art/experimental/estimators/__init__.py @@ -1,4 +1,5 @@ """ Experimental Estimator API """ +from art.experimental.estimators import huggingface_multimodal from art.experimental.estimators.jax import JaxEstimator diff --git a/art/experimental/estimators/huggingface_multimodal/__init__.py b/art/experimental/estimators/huggingface_multimodal/__init__.py new file mode 100644 index 0000000000..22d1ff20c4 --- /dev/null +++ b/art/experimental/estimators/huggingface_multimodal/__init__.py @@ -0,0 +1,5 @@ +""" +Module containing estimators for CLIP. +""" +from art.experimental.estimators.huggingface_multimodal.huggingface_mm import HFMMPyTorch +from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput diff --git a/art/estimators/hf_mm/huggingface_mm.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py similarity index 89% rename from art/estimators/hf_mm/huggingface_mm.py rename to art/experimental/estimators/huggingface_multimodal/huggingface_mm.py index 45f021981a..b2c4d0228b 100644 --- a/art/estimators/hf_mm/huggingface_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py @@ -35,7 +35,7 @@ from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE from art.defences.preprocessor.preprocessor import Preprocessor from art.defences.postprocessor.postprocessor import Postprocessor - from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput + from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput logger = logging.getLogger(__name__) @@ -147,11 +147,11 @@ def device(self) -> "torch.device": @staticmethod def _preprocess_and_convert_inputs( - x: "MultiModalHuggingFaceInput", + x: "HuggingFaceMultiModalInput", y: Optional[Union[np.ndarray, "torch.Tensor"]] = None, fit: bool = False, # pylint: disable=W0613 no_grad: bool = True, # pylint: disable=W0613 - ) -> Tuple["MultiModalHuggingFaceInput", Union[np.ndarray, "torch.Tensor", None]]: + ) -> Tuple["HuggingFaceMultiModalInput", Union[np.ndarray, "torch.Tensor", None]]: """ Dummy function to allow compatibility with ART attacks. All pre-processing should be done before by the relevant HF pre-processor. @@ -165,7 +165,7 @@ def _preprocess_and_convert_inputs( """ return x, y - def _get_losses(self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "torch.Tensor"]) -> "torch.Tensor": + def _get_losses(self, x: "HuggingFaceMultiModalInput", y: Union[np.ndarray, "torch.Tensor"]) -> "torch.Tensor": """ Get the loss tensor output of the model including all preprocessing. @@ -198,7 +198,7 @@ def _get_losses(self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "tor return self.loss_fn(preds, y) def loss_gradient( # pylint: disable=W0613 - self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "torch.Tensor"], **kwargs + self, x: "HuggingFaceMultiModalInput", y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> np.ndarray: """ Compute the gradient of the loss function w.r.t. the image component of the input @@ -237,7 +237,7 @@ def loss_gradient( # pylint: disable=W0613 return grads.cpu().numpy() def predict( - self, x: Union["MultiModalHuggingFaceInput", np.ndarray], batch_size: int = 128, **kwargs + self, x: Union["HuggingFaceMultiModalInput", np.ndarray], batch_size: int = 128, **kwargs ) -> np.ndarray: """ Perform prediction for a batch of inputs. @@ -246,12 +246,14 @@ def predict( :param batch_size: Batch size. :return: """ - from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput + from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput # Set model to evaluation mode self._model.eval() if isinstance(x, np.ndarray): - raise ValueError("x should be of type art.estimators.hf_mm.hf_inputs.MultiModalHuggingFaceInput") + raise ValueError( + "x should be of type art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs.HuggingFaceMultiModalInput" + ) x_preprocessed, _ = self._preprocess_and_convert_inputs(x=x, y=None, fit=False, no_grad=True) num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size))) @@ -259,17 +261,19 @@ def predict( for m in tqdm(range(num_batch)): x_batch = x[batch_size * m : batch_size * (m + 1)] x_batch = x_batch.to(self._device) - if isinstance(x_batch, MultiModalHuggingFaceInput): + if isinstance(x_batch, HuggingFaceMultiModalInput): predictions = self._model(**x_batch) else: - raise ValueError("expected art.estimators.hf_mm.hf_inputs.MultiModalHuggingFaceInput") + raise ValueError( + "expected art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs.HuggingFaceMultiModalInput" + ) results.append(predictions.logits_per_image.cpu().detach().numpy()) return np.concatenate(results) def fit( # pylint: disable=W0221 self, - x: Union[np.ndarray, "MultiModalHuggingFaceInput"], + x: Union[np.ndarray, "HuggingFaceMultiModalInput"], y: Union[np.ndarray, "torch.Tensor"], batch_size: int = 128, nb_epochs: int = 10, @@ -282,7 +286,7 @@ def fit( # pylint: disable=W0221 Fit the classifier on the training set """ import torch - from art.estimators.hf_mm.hf_inputs import MultiModalHuggingFaceInput + from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput self._model.train() if self._optimizer is None: @@ -315,10 +319,12 @@ def fit( # pylint: disable=W0221 # Perform prediction try: - if isinstance(x_batch, MultiModalHuggingFaceInput): + if isinstance(x_batch, HuggingFaceMultiModalInput): model_outputs = self._model(**x_batch) else: - raise ValueError("expected art.estimators.hf_mm.hf_inputs.MultiModalHuggingFaceInput") + raise ValueError( + "expected art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs.HuggingFaceMultiModalInput" + ) except ValueError as err: if "Expected more than 1 value per channel when training" in str(err): logger.exception( @@ -357,7 +363,7 @@ def get_activations( raise NotImplementedError def compute_loss( # type: ignore - self, x: "MultiModalHuggingFaceInput", y: Union[np.ndarray, "torch.Tensor"], **kwargs + self, x: "HuggingFaceMultiModalInput", y: Union[np.ndarray, "torch.Tensor"], **kwargs ) -> Union[np.ndarray, "torch.Tensor"]: """ Compute the loss of the neural network for samples `x`. diff --git a/art/estimators/hf_mm/hf_inputs.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py similarity index 85% rename from art/estimators/hf_mm/hf_inputs.py rename to art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py index 9184630565..458c4fff63 100644 --- a/art/estimators/hf_mm/hf_inputs.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py @@ -31,7 +31,7 @@ import torch -class MultiModalHuggingFaceInput(UserDict): +class HuggingFaceMultiModalInput(UserDict): """ Custom class to allow HF inputs which are in a dictionary to be compatible with ART. Allows certain array-like functionality to be performed directly onto the input such as @@ -70,7 +70,7 @@ def __setitem__(self, key, value): pixel_values = UserDict.__getitem__(self, "pixel_values") original_shape = pixel_values.shape with torch.no_grad(): - if isinstance(value, MultiModalHuggingFaceInput): + if isinstance(value, HuggingFaceMultiModalInput): pixel_values[key] = value["pixel_values"] else: pixel_values[key] = torch.tensor(value) @@ -85,39 +85,39 @@ def __setitem__(self, key, value): f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" ) - def __getitem__(self, item: Union[slice, Tuple, int, str]) -> Union[MultiModalHuggingFaceInput, "torch.Tensor"]: + def __getitem__(self, item: Union[slice, Tuple, int, str]) -> Union[HuggingFaceMultiModalInput, "torch.Tensor"]: # print('__getitem__ key ', item) # print('with type ', type(item)) if isinstance(item, (slice, tuple)): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output if isinstance(item, int): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output if isinstance(item, np.ndarray): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output if item in self.keys(): return UserDict.__getitem__(self, item) raise ValueError("Unsupported item for __getitem__ in ARTInput") - def __add__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> MultiModalHuggingFaceInput: + def __add__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> HuggingFaceMultiModalInput: import torch pixel_values = UserDict.__getitem__(self, "pixel_values") dev_id = pixel_values.get_device() with torch.no_grad(): - if isinstance(other, MultiModalHuggingFaceInput): + if isinstance(other, HuggingFaceMultiModalInput): if dev_id == -1: pixel_values = pixel_values + other["pixel_values"].to("cpu") else: @@ -127,24 +127,24 @@ def __add__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> Multi pixel_values = pixel_values + torch.tensor(other) else: pixel_values = pixel_values + torch.tensor(other).to("cuda:" + str(dev_id)) - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output - def __sub__(self, other: MultiModalHuggingFaceInput) -> MultiModalHuggingFaceInput: - if isinstance(other, MultiModalHuggingFaceInput): + def __sub__(self, other: HuggingFaceMultiModalInput) -> HuggingFaceMultiModalInput: + if isinstance(other, HuggingFaceMultiModalInput): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values - other["pixel_values"] - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values else: raise ValueError("Unsupported type for __sub__ in ARTInput") return output - def __mul__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> MultiModalHuggingFaceInput: + def __mul__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> HuggingFaceMultiModalInput: import torch - if isinstance(other, MultiModalHuggingFaceInput): + if isinstance(other, HuggingFaceMultiModalInput): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values * other["pixel_values"] elif isinstance(other, np.ndarray): @@ -153,7 +153,7 @@ def __mul__(self, other: Union[MultiModalHuggingFaceInput, np.ndarray]) -> Multi else: raise ValueError("Unsupported type for __mul__ in ARTInput") - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output @@ -161,7 +161,7 @@ def __len__(self): pixel_values = UserDict.__getitem__(self, "pixel_values") return len(pixel_values) - def reshape(self, new_shape: Tuple) -> MultiModalHuggingFaceInput: + def reshape(self, new_shape: Tuple) -> HuggingFaceMultiModalInput: """ Defines reshaping on the HF input. :param new_shape: The new shape for the input @@ -172,15 +172,15 @@ def reshape(self, new_shape: Tuple) -> MultiModalHuggingFaceInput: pixel_values = UserDict.__getitem__(self, "pixel_values") if not isinstance(pixel_values, torch.Tensor): pixel_values = torch.tensor(pixel_values) - output = MultiModalHuggingFaceInput(**self) + output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = torch.reshape(pixel_values, new_shape) return output - def to(self, device: Union["torch.device", str]) -> MultiModalHuggingFaceInput: # pylint: disable=C0103 + def to(self, device: Union["torch.device", str]) -> HuggingFaceMultiModalInput: # pylint: disable=C0103 """ Moves tensors to the supplied device :param device: device to move the tensors to. - :return: A MultiModalHuggingFaceInput instance with tensors moved to the supplied device + :return: A HuggingFaceMultiModalInput instance with tensors moved to the supplied device """ for key in self.keys(): self[key] = self[key].to(device) diff --git a/art_adv_1.npy b/art_adv_1.npy new file mode 100644 index 0000000000000000000000000000000000000000..a81c6cd92a7837c4a65f0ff6bb6b77c80fdd101d GIT binary patch literal 602240 zcmce<&5l3alAUFyT!m*1l4bE_2_aJI5i<}I28@smwiqCcY{UeRxCg|2ATf1H9x`YU zcVaGq{j82>d*!bDRn>d?n3h5jv0}xF*gG?;{_of6|Mfrl&;H~8^gsXC{?`BSxBkb! z_^;x}I{o9;X3-AB_Yfl{ezg}{%!EZEIe6K!y@b>6*POLV) z_DvivI}Gps)cbaZzqS64dsz4WcBh~2sQ2mE+4!8(eeqyz3(K>j_O{y(SodVl;#(7c z^H=eSyvf69IDsjGoEw!ZV3ZQk71*l$Tc+H&9X* 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zi|2494~Enar;hr3dL|dgh8cks>p92?mjC8GzjEHWmY09*)N793iTcD|@8^g!+I7xb zKXAkKzPsAE#ag4fv$ek1>EV0$%rNJ3?fU4>l)Zgs7S?hIOxMNp4?h0JMklv1ah&y6 zPdxh+W0S_J>-*%8b2!7j*u3Sj^~?XGEbdD^HBMdoQp5Sj#!rg5`1+jlGx$9xcg9)a z$iK({Qr^CAF;d>@=Hnhae7%pj*f_-7`}ng@YZ!a0 zc2Ae5vufS0vsWD3Lu%@S@Y6VV57`s`D{=|n>U{S;+goRbW4SWN!N2csu-Ow1eRthG zblsV^ANymAe`>uK_cz^q@$Q4>-*@xwxp98db+KJn&tA+gxY6OSE@rfu7ZV$1brh@@OPh$jm8m~*lY7y6LqGq+*NN*%zE}0yYJ9iUvx!1wXT18 z+Y7uor51bpjQ<%f{N)kD*Z$@4HOG8#OrCf4a= np.min(original_image)) + assert np.all(art_adv <= np.max(original_image)) + assert np.all(manual_adv >= np.min(original_image)) + assert np.all(manual_adv <= np.max(original_image)) + # eps = norm_bound_eps() + + eps_mins = original_image - 0.3 + eps_maxs = original_image + 0.3 + + # for a, e in zip(art_adv, eps_mins): + # assert np.all(a >= e) + + assert np.all(art_adv >= eps_mins) + assert np.all(art_adv <= eps_maxs) + assert np.all(manual_adv >= eps_mins) + assert np.all(manual_adv <= eps_maxs) + + art_delta = art_adv - original_image + target = manual_adv - original_image + # np.save('art_adv_' + str(max_iter) + '.npy', art_delta) + # target = np.load('art_adv_' + str(max_iter) + '.npy') + diff_count = 0 + diff = np.abs(art_delta - target) + for d in diff: + if d >= 0.0001: + # if not np.allclose(d, 0, rtol=1e-04, atol=1e-03): + assert d <= 0.11 # one grad step + diff_count += 1 + assert diff_count < 10 + assert np.allclose(art_delta, target, rtol=1e-04, atol=1e-03) """ @@ -289,7 +333,7 @@ def manual_attack(): def test_predict(): import torch from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput + from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) @@ -301,5 +345,5 @@ def test_predict(): clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224), ) - inputs = MultiModalHuggingFaceInput(**inputs) + inputs = HuggingFaceMultiModalInput(**inputs) preds = art_classifier.predict(inputs) From f5774b55d1652f579c1f69717d8b026452d61d6b Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Wed, 25 Oct 2023 22:37:06 +0100 Subject: [PATCH 11/46] run ci Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index c0737d8524..0fe1ec7ffe 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -17,6 +17,7 @@ on: - main - dev* - clip_attack + - CI_run # Run scheduled CI flow daily schedule: From 9aa13651e1c719b67b32dd8739ce4abbf738c319 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Wed, 25 Oct 2023 22:44:51 +0100 Subject: [PATCH 12/46] commenting and formatting edits Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 3 +- .github/workflows/ci-style-checks.yml | 3 +- .../projected_gradient_descent_numpy.py | 1 - art/defences/trainer/adversarial_trainer.py | 18 ++++---- .../huggingface_multimodal/huggingface_mm.py | 15 ++----- clip_dev.py | 43 ++++++++----------- .../attacks/evasion/test_multimodal_attack.py | 29 ++++--------- 7 files changed, 43 insertions(+), 69 deletions(-) diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index 0fe1ec7ffe..c9803c6ec9 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -16,8 +16,7 @@ on: branches: - main - dev* - - clip_attack - - CI_run + - clip_checking # Run scheduled CI flow daily schedule: diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml index 6aac12d741..80e2529a0b 100644 --- a/.github/workflows/ci-style-checks.yml +++ b/.github/workflows/ci-style-checks.yml @@ -16,7 +16,8 @@ on: branches: - main - dev* - - clip_attack + - clip_checking + # Run scheduled CI flow daily schedule: diff --git a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index e6fd6df8d4..be2a7db5ec 100644 --- a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -410,7 +410,6 @@ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> n # Start to compute adversarial examples if x.dtype == object: - # adv_x = x.copy() adv_x = copy.deepcopy(x) else: adv_x = x.astype(ART_NUMPY_DTYPE) diff --git a/art/defences/trainer/adversarial_trainer.py b/art/defences/trainer/adversarial_trainer.py index a93ffbb2a9..ad98e763b9 100644 --- a/art/defences/trainer/adversarial_trainer.py +++ b/art/defences/trainer/adversarial_trainer.py @@ -206,7 +206,7 @@ def fit( # pylint: disable=W0221 ind = np.arange(len(x)) attack_id = 0 from tqdm import tqdm - import torch + # Precompute adversarial samples for transferred attacks logged = False self._precomputed_adv_samples = [] @@ -223,12 +223,12 @@ def fit( # pylint: disable=W0221 else: self._precomputed_adv_samples.append(None) - for epoch in trange(nb_epochs, desc="Adversarial training epochs"): + for _ in trange(nb_epochs, desc="Adversarial training epochs"): # Shuffle the examples # np.random.shuffle(ind) pbar = tqdm(range(nb_batches)) - self._classifier.training_loss = [] - self._classifier.training_accuracy = [] + self._classifier.training_loss = [] # type: ignore + self._classifier.training_accuracy = [] # type: ignore for batch_id in pbar: # Create batch data @@ -259,11 +259,13 @@ def fit( # pylint: disable=W0221 x_batch[adv_ids] = x_adv # Fit batch - self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=False, **kwargs) + self._classifier.fit( + x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=False, **kwargs + ) pbar.set_description( - f"Loss {np.mean(np.stack(self._classifier.training_loss)):.2f} " - f"Acc {np.mean(self._classifier.training_accuracy):.2f} " - ) + f"Loss {np.mean(np.stack(self._classifier.training_loss)):.2f} " # type: ignore + f"Acc {np.mean(self._classifier.training_accuracy):.2f} " # type: ignore + ) attack_id = (attack_id + 1) % len(self.attacks) # torch.save(self._classifier.model.state_dict(), 'clip_adv_trained_' + str(epoch) + '.pt') # torch.save(self._classifier._optimizer.state_dict(), 'clip__opt_adv_trained_' + str(epoch) + '.pt') diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py index b2c4d0228b..2dbb091a31 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py @@ -251,9 +251,7 @@ def predict( # Set model to evaluation mode self._model.eval() if isinstance(x, np.ndarray): - raise ValueError( - "x should be of type art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs.HuggingFaceMultiModalInput" - ) + raise ValueError("x should be of type HuggingFaceMultiModalInput") x_preprocessed, _ = self._preprocess_and_convert_inputs(x=x, y=None, fit=False, no_grad=True) num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size))) @@ -264,9 +262,7 @@ def predict( if isinstance(x_batch, HuggingFaceMultiModalInput): predictions = self._model(**x_batch) else: - raise ValueError( - "expected art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs.HuggingFaceMultiModalInput" - ) + raise ValueError("expected type HuggingFaceMultiModalInput") results.append(predictions.logits_per_image.cpu().detach().numpy()) return np.concatenate(results) @@ -277,7 +273,6 @@ def fit( # pylint: disable=W0221 y: Union[np.ndarray, "torch.Tensor"], batch_size: int = 128, nb_epochs: int = 10, - drop_last: bool = False, scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"] = None, verbose: bool = True, **kwargs, @@ -295,7 +290,7 @@ def fit( # pylint: disable=W0221 y_tensor = torch.from_numpy(y) - num_batch = int(np.ceil(len(y_tensor) / float(batch_size))) + num_batch = int(len(y_tensor) / float(batch_size)) ind = np.arange(len(y_tensor)) # Start training @@ -322,9 +317,7 @@ def fit( # pylint: disable=W0221 if isinstance(x_batch, HuggingFaceMultiModalInput): model_outputs = self._model(**x_batch) else: - raise ValueError( - "expected art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs.HuggingFaceMultiModalInput" - ) + raise ValueError("expected type HuggingFaceMultiModalInput") except ValueError as err: if "Expected more than 1 value per channel when training" in str(err): logger.exception( diff --git a/clip_dev.py b/clip_dev.py index 0f65b9497c..e30e92fbed 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,7 +1,6 @@ import numpy as np from art.estimators.hf_mm import HFMMPyTorch from art.estimators.hf_mm import MultiModalHuggingFaceInput -import matplotlib.pyplot as plt from art.attacks.evasion import ProjectedGradientDescent @@ -60,8 +59,8 @@ def norm_bound_eps(eps_bound=None): def get_cifar_data(): - train_set = datasets.CIFAR10('./data', train=True, download=True) - test_set = datasets.CIFAR10('./data', train=False, download=True) + train_set = datasets.CIFAR10("./data", train=True, download=True) + test_set = datasets.CIFAR10("./data", train=False, download=True) x_train = train_set.data.astype(np.float32) y_train = np.asarray(train_set.targets) @@ -139,20 +138,14 @@ def cifar_clip_pgd(): import requests from transformers import CLIPProcessor, CLIPModel - image_list = ['000000039769.jpg', - '000000000285.jpg', - '000000002006.jpg', - '000000002149.jpg', - '000000005992.jpg', - '000000011615.jpg', - '000000013597.jpg'] + text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] labels = torch.tensor(np.asarray([0, 1, 3])) input_list = [] - for fname in ['000000039769.jpg', '000000000285.jpg', '000000002006.jpg', '000000002149.jpg']: - url = 'http://images.cocodataset.org/val2017/' + fname + for fname in ["000000039769.jpg", "000000000285.jpg", "000000002006.jpg", "000000002149.jpg"]: + url = "http://images.cocodataset.org/val2017/" + fname input_list.append(Image.open(requests.get(url, stream=True).raw)) model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") @@ -194,7 +187,6 @@ def test_fit(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() @@ -206,9 +198,8 @@ def test_fit(): optimizer=optimizer, nb_classes=10, loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), - np.max(original_image)), - input_shape=(3, 224, 224) + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), ) num_of_samples = len(inputs) @@ -227,12 +218,14 @@ def test_predict(): art_classifier = HFMMPyTorch( model, nb_classes=num_classes, - loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), ) inputs = MultiModalHuggingFaceInput(**inputs) preds = art_classifier.predict(inputs) - print('Pred shape is ', preds.shape) + print("Pred shape is ", preds.shape) def test_adv_train(): @@ -249,15 +242,13 @@ def test_adv_train(): model, nb_classes=num_classes, optimizer=optimizer, - loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224) + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), + ) + trainer = AdversarialTrainerMadryPGD( + art_classifier, nb_epochs=10, eps=8 / 255, eps_step=1 / 255, max_iter=10, num_random_init=0 ) - trainer = AdversarialTrainerMadryPGD(art_classifier, - nb_epochs=10, - eps=8/255, - eps_step=1/255, - max_iter=10, - num_random_init=0) inputs = MultiModalHuggingFaceInput(**inputs) trainer.fit(inputs, labels.detach().cpu().numpy()) diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 5b0fffd62a..8a9d783101 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -176,8 +176,7 @@ def manual_attack(): @pytest.mark.only_with_platform("huggingface") @pytest.mark.parametrize("max_iter", [1, 5]) -@pytest.mark.parametrize("to_one_hot", [False]) -def test_equivalence(max_iter, to_one_hot): +def test_equivalence(max_iter): """ Test that the result from using ART tools matches that obtained by manual calculation. """ @@ -195,7 +194,7 @@ def attack_clip(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() - inputs, original_image, labels, num_classes = get_and_process_input(to_one_hot=to_one_hot, return_batch=False) + inputs, original_image, labels, num_classes = get_and_process_input() original_image = inputs.pixel_values.clone().cpu().numpy() my_input = HuggingFaceMultiModalInput(**inputs) @@ -278,20 +277,20 @@ def manual_attack(): return adv_current - inputs, original_image, labels, num_classes = get_and_process_input(to_one_hot=to_one_hot, return_batch=False) + inputs, original_image, labels, num_classes = get_and_process_input() manual_adv = manual_attack() art_adv = attack_clip() art_adv = art_adv["pixel_values"] art_adv = art_adv.cpu().detach().numpy() - + art_adv = art_adv.flatten() original_image = original_image.flatten() manual_adv = manual_adv.flatten() - ''' + """ Assert valid adversarial examples - ''' + """ assert np.all(art_adv >= np.min(original_image)) assert np.all(art_adv <= np.max(original_image)) assert np.all(manual_adv >= np.min(original_image)) @@ -301,9 +300,6 @@ def manual_attack(): eps_mins = original_image - 0.3 eps_maxs = original_image + 0.3 - # for a, e in zip(art_adv, eps_mins): - # assert np.all(a >= e) - assert np.all(art_adv >= eps_mins) assert np.all(art_adv <= eps_maxs) assert np.all(manual_adv >= eps_mins) @@ -313,15 +309,8 @@ def manual_attack(): target = manual_adv - original_image # np.save('art_adv_' + str(max_iter) + '.npy', art_delta) # target = np.load('art_adv_' + str(max_iter) + '.npy') - diff_count = 0 - diff = np.abs(art_delta - target) - for d in diff: - if d >= 0.0001: - # if not np.allclose(d, 0, rtol=1e-04, atol=1e-03): - assert d <= 0.11 # one grad step - diff_count += 1 - assert diff_count < 10 - assert np.allclose(art_delta, target, rtol=1e-04, atol=1e-03) + + assert np.allclose(art_delta, target, rtol=1e-04, atol=1e-04) """ @@ -346,4 +335,4 @@ def test_predict(): input_shape=(3, 224, 224), ) inputs = HuggingFaceMultiModalInput(**inputs) - preds = art_classifier.predict(inputs) + _ = art_classifier.predict(inputs) From 109905c9102ae3e6b735ab7f894fe6a6ad774204 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 26 Oct 2023 14:42:12 +0100 Subject: [PATCH 13/46] move pgd changes to experimental Signed-off-by: GiulioZizzo --- art/experimental/attacks/__init__.py | 1 + art/experimental/attacks/evasion/__init__.py | 3 + .../attacks/evasion/fast_gradient.py | 257 ++++++++++++++++++ .../projected_gradient_descent/__init__.py | 0 .../projected_gradient_descent_numpy.py | 216 +++++++++++++++ .../attacks/evasion/test_multimodal_attack.py | 8 +- 6 files changed, 481 insertions(+), 4 deletions(-) create mode 100644 art/experimental/attacks/__init__.py create mode 100644 art/experimental/attacks/evasion/__init__.py create mode 100644 art/experimental/attacks/evasion/fast_gradient.py create mode 100644 art/experimental/attacks/evasion/projected_gradient_descent/__init__.py create mode 100644 art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py diff --git a/art/experimental/attacks/__init__.py b/art/experimental/attacks/__init__.py new file mode 100644 index 0000000000..f11bdc8228 --- /dev/null +++ b/art/experimental/attacks/__init__.py @@ -0,0 +1 @@ +from art.experimental.attacks import evasion diff --git a/art/experimental/attacks/evasion/__init__.py b/art/experimental/attacks/evasion/__init__.py new file mode 100644 index 0000000000..cdfb5ef51c --- /dev/null +++ b/art/experimental/attacks/evasion/__init__.py @@ -0,0 +1,3 @@ +from art.experimental.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import ( + CLIPProjectedGradientDescentNumpy, +) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py new file mode 100644 index 0000000000..d48ecb77d6 --- /dev/null +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -0,0 +1,257 @@ +import numpy as np +import copy + +from art.attacks.evasion.fast_gradient import FastGradientMethod +from typing import Optional, Union, TYPE_CHECKING +from art.attacks.attack import EvasionAttack +from art.estimators.estimator import BaseEstimator, LossGradientsMixin +from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput + +from art.summary_writer import SummaryWriter +from art.config import ART_NUMPY_DTYPE + +from art.utils import ( + compute_success, + get_labels_np_array, + random_sphere, + projection, + check_and_transform_label_format, +) + + +class FastGradientMethodCLIP(FastGradientMethod): + + attack_params = EvasionAttack.attack_params + [ + "norm", + "eps", + "eps_step", + "targeted", + "num_random_init", + "batch_size", + "minimal", + "summary_writer", + ] + _estimator_requirements = (BaseEstimator, LossGradientsMixin) + + def __init__( + self, + estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE", + norm: Union[int, float, str] = np.inf, + eps: Union[int, float, np.ndarray] = 0.3, + eps_step: Union[int, float, np.ndarray] = 0.1, + targeted: bool = False, + num_random_init: int = 0, + batch_size: int = 32, + minimal: bool = False, + summary_writer: Union[str, bool, SummaryWriter] = False, + ) -> None: + + super().__init__( + estimator=estimator, + norm=norm, + eps=eps, + eps_step=eps_step, + targeted=targeted, + num_random_init=num_random_init, + batch_size=batch_size, + minimal=minimal, + summary_writer=summary_writer, + ) + + def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) -> np.ndarray: + """ + Iteratively compute the minimal perturbation necessary to make the class prediction change. Stop when the + first adversarial example was found. + + :param x: An array with the original inputs. + :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes). + :return: An array holding the adversarial examples. + """ + adv_x = copy.deepcopy(x) + + # Compute perturbation with implicit batching + for batch_id in range(int(np.ceil(adv_x.shape[0] / float(self.batch_size)))): + batch_index_1, batch_index_2 = ( + batch_id * self.batch_size, + (batch_id + 1) * self.batch_size, + ) + batch = adv_x[batch_index_1:batch_index_2] + batch_labels = y[batch_index_1:batch_index_2] + + mask_batch = mask + if mask is not None: + # Here we need to make a distinction: if the masks are different for each input, we need to index + # those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is. + if len(mask.shape) == len(x.shape): + mask_batch = mask[batch_index_1:batch_index_2] + + # Get perturbation + perturbation = self._compute_perturbation(batch, batch_labels, mask_batch) + + # Get current predictions + active_indices = np.arange(len(batch)) + + if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray): + if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]: + current_eps = self.eps_step[batch_index_1:batch_index_2] + partial_stop_condition = (current_eps <= self.eps[batch_index_1:batch_index_2]).all() + + else: + current_eps = self.eps_step + partial_stop_condition = (current_eps <= self.eps).all() + + else: + current_eps = self.eps_step + partial_stop_condition = current_eps <= self.eps + + while active_indices.size > 0 and partial_stop_condition: + # Adversarial crafting + current_x = self._apply_perturbation(x[batch_index_1:batch_index_2], perturbation, current_eps) + + # Update + batch[active_indices] = current_x[active_indices] + adv_preds = self.estimator.predict(batch) + + # If targeted active check to see whether we have hit the target, otherwise head to anything but + if self.targeted: + active_indices = np.where(np.argmax(batch_labels, axis=1) != np.argmax(adv_preds, axis=1))[0] + else: + active_indices = np.where(np.argmax(batch_labels, axis=1) == np.argmax(adv_preds, axis=1))[0] + + # Update current eps and check the stop condition + if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray): + if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]: + current_eps = current_eps + self.eps_step[batch_index_1:batch_index_2] + partial_stop_condition = (current_eps <= self.eps[batch_index_1:batch_index_2]).all() + + else: + current_eps = current_eps + self.eps_step + partial_stop_condition = (current_eps <= self.eps).all() + + else: + current_eps = current_eps + self.eps_step + partial_stop_condition = current_eps <= self.eps + + adv_x[batch_index_1:batch_index_2] = batch + + return adv_x + + def _apply_perturbation( + self, x: np.ndarray, perturbation: np.ndarray, eps_step: Union[int, float, np.ndarray] + ) -> np.ndarray: + + perturbation_step = eps_step * perturbation + if perturbation_step.dtype != object: + perturbation_step[np.isnan(perturbation_step)] = 0 + else: + for i, _ in enumerate(perturbation_step): + perturbation_step_i_array = perturbation_step[i].astype(np.float32) + if np.isnan(perturbation_step_i_array).any(): + perturbation_step[i] = np.where( + np.isnan(perturbation_step_i_array), 0.0, perturbation_step_i_array + ).astype(object) + + x = x + perturbation_step + if self.estimator.clip_values is not None: + clip_min, clip_max = self.estimator.clip_values + if x.dtype == object: + if isinstance(x, HuggingFaceMultiModalInput): + for i_obj in range(x.shape[0]): + x[i_obj] = np.clip(x[i_obj]["pixel_values"].cpu().detach().numpy(), clip_min, clip_max) + else: + for i_obj in range(x.shape[0]): + x[i_obj] = np.clip(x[i_obj], clip_min, clip_max) + else: + x = np.clip(x, clip_min, clip_max) + + return x + + def _compute( + self, + x: np.ndarray, + x_init: np.ndarray, + y: np.ndarray, + mask: Optional[np.ndarray], + eps: Union[int, float, np.ndarray], + eps_step: Union[int, float, np.ndarray], + project: bool, + random_init: bool, + batch_id_ext: Optional[int] = None, + decay: Optional[float] = None, + momentum: Optional[np.ndarray] = None, + ) -> np.ndarray: + if random_init: + n = x.shape[0] + m = np.prod(x.shape[1:]).item() + random_perturbation = random_sphere(n, m, eps, self.norm).reshape(x.shape).astype(ART_NUMPY_DTYPE) + if mask is not None: + random_perturbation = random_perturbation * (mask.astype(ART_NUMPY_DTYPE)) + x_adv = x.astype(ART_NUMPY_DTYPE) + random_perturbation + + if self.estimator.clip_values is not None: + clip_min, clip_max = self.estimator.clip_values + x_adv = np.clip(x_adv, clip_min, clip_max) + else: + if x.dtype == object: + x_adv = copy.deepcopy(x) + else: + x_adv = x.astype(ART_NUMPY_DTYPE) + + # Compute perturbation with implicit batching + for batch_id in range(int(np.ceil(x.shape[0] / float(self.batch_size)))): + if batch_id_ext is None: + self._batch_id = batch_id + else: + self._batch_id = batch_id_ext + batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size + batch_index_2 = min(batch_index_2, x.shape[0]) + batch = x_adv[batch_index_1:batch_index_2] + batch_labels = y[batch_index_1:batch_index_2] + + mask_batch = mask + if mask is not None: + # Here we need to make a distinction: if the masks are different for each input, we need to index + # those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is. + if len(mask.shape) == len(x.shape): + mask_batch = mask[batch_index_1:batch_index_2] + + # Get perturbation + perturbation = self._compute_perturbation(batch, batch_labels, mask_batch, decay, momentum) + + # Compute batch_eps and batch_eps_step + if isinstance(eps, np.ndarray) and isinstance(eps_step, np.ndarray): + if len(eps.shape) == len(x.shape) and eps.shape[0] == x.shape[0]: + batch_eps = eps[batch_index_1:batch_index_2] + batch_eps_step = eps_step[batch_index_1:batch_index_2] + + else: + batch_eps = eps + batch_eps_step = eps_step + + else: + batch_eps = eps + batch_eps_step = eps_step + + # Apply perturbation and clip + x_adv[batch_index_1:batch_index_2] = self._apply_perturbation(batch, perturbation, batch_eps_step) + + if project: + if x_adv.dtype == object: + for i_sample in range(batch_index_1, batch_index_2): + if isinstance(batch_eps, np.ndarray) and batch_eps.shape[0] == x_adv.shape[0]: + perturbation = projection( + x_adv[i_sample] - x_init[i_sample], batch_eps[i_sample], self.norm + ) + + else: + perturbation = projection(x_adv[i_sample] - x_init[i_sample], batch_eps, self.norm) + + x_adv[i_sample] = x_init[i_sample] + perturbation + + else: + perturbation = projection( + x_adv[batch_index_1:batch_index_2] - x_init[batch_index_1:batch_index_2], batch_eps, self.norm + ) + x_adv[batch_index_1:batch_index_2] = x_init[batch_index_1:batch_index_2] + perturbation + + return x_adv diff --git a/art/experimental/attacks/evasion/projected_gradient_descent/__init__.py b/art/experimental/attacks/evasion/projected_gradient_descent/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py new file mode 100644 index 0000000000..c859acd933 --- /dev/null +++ b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -0,0 +1,216 @@ +from __future__ import absolute_import, division, print_function, unicode_literals + +import copy +from typing import Optional, Union, TYPE_CHECKING + +import numpy as np +from tqdm.auto import trange + +from art.config import ART_NUMPY_DTYPE +from art.estimators.classification.classifier import ClassifierMixin +from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format, compute_success_array + +if TYPE_CHECKING: + from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE, OBJECT_DETECTOR_TYPE + +from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import ( + ProjectedGradientDescentNumpy, +) + +from art.summary_writer import SummaryWriter + +if TYPE_CHECKING: + from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE, OBJECT_DETECTOR_TYPE + + +class CLIPProjectedGradientDescentNumpy(ProjectedGradientDescentNumpy): + def __init__( + self, + estimator: Union["CLASSIFIER_LOSS_GRADIENTS_TYPE", "OBJECT_DETECTOR_TYPE"], + norm: Union[int, float, str] = np.inf, + eps: Union[int, float, np.ndarray] = 0.3, + eps_step: Union[int, float, np.ndarray] = 0.1, + decay: Optional[float] = None, + max_iter: int = 100, + targeted: bool = False, + num_random_init: int = 0, + batch_size: int = 32, + random_eps: bool = False, + summary_writer: Union[str, bool, SummaryWriter] = False, + verbose: bool = True, + ) -> None: + """ + Create a :class:`.ProjectedGradientDescentNumpy` instance. + + :param estimator: An trained estimator. + :param norm: The norm of the adversarial perturbation supporting "inf", np.inf, 1 or 2. + :param eps: Maximum perturbation that the attacker can introduce. + :param eps_step: Attack step size (input variation) at each iteration. + :param random_eps: When True, epsilon is drawn randomly from truncated normal distribution. The literature + suggests this for FGSM based training to generalize across different epsilons. eps_step + is modified to preserve the ratio of eps / eps_step. The effectiveness of this method with + PGD is untested (https://arxiv.org/pdf/1611.01236.pdf). + :param max_iter: The maximum number of iterations. + :param targeted: Indicates whether the attack is targeted (True) or untargeted (False) + :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting + at the original input. + :param batch_size: Size of the batch on which adversarial samples are generated. + :param summary_writer: Activate summary writer for TensorBoard. + Default is `False` and deactivated summary writer. + If `True` save runs/CURRENT_DATETIME_HOSTNAME in current directory. + If of type `str` save in path. + If of type `SummaryWriter` apply provided custom summary writer. + Use hierarchical folder structure to compare between runs easily. e.g. pass in + ‘runs/exp1’, ‘runs/exp2’, etc. for each new experiment to compare across them. + :param verbose: Show progress bars. + """ + if summary_writer and num_random_init > 1: + raise ValueError("TensorBoard is not yet supported for more than 1 random restart (num_random_init>1).") + + super().__init__( + estimator=estimator, + norm=norm, + eps=eps, + eps_step=eps_step, + decay=decay, + max_iter=max_iter, + targeted=targeted, + num_random_init=num_random_init, + batch_size=batch_size, + random_eps=random_eps, + summary_writer=summary_writer, + verbose=verbose, + ) + + def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray: + """ + Generate adversarial samples and return them in an array. + + :param x: An array with the original inputs. + :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape + (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial + samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect + (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`. + + :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations. + Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any + features for which the mask is zero will not be adversarially perturbed. + :type mask: `np.ndarray` + :return: An array holding the adversarial examples. + """ + mask = self._get_mask(x, **kwargs) + + # Ensure eps is broadcastable + self._check_compatibility_input_and_eps(x=x) + + # Check whether random eps is enabled + self._random_eps() + + if isinstance(self.estimator, ClassifierMixin): + # Set up targets + targets = self._set_targets(x, y) + + # Start to compute adversarial examples + adv_x = x.astype(ART_NUMPY_DTYPE) + + for batch_id in range(int(np.ceil(x.shape[0] / float(self.batch_size)))): + + self._batch_id = batch_id + + for rand_init_num in trange( + max(1, self.num_random_init), desc="PGD - Random Initializations", disable=not self.verbose + ): + batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size + batch_index_2 = min(batch_index_2, x.shape[0]) + batch = x[batch_index_1:batch_index_2] + batch_labels = targets[batch_index_1:batch_index_2] + mask_batch = mask + + if mask is not None: + if len(mask.shape) == len(x.shape): + mask_batch = mask[batch_index_1:batch_index_2] + + momentum = np.zeros(batch.shape) + + for i_max_iter in trange( + self.max_iter, desc="PGD - Iterations", leave=False, disable=not self.verbose + ): + self._i_max_iter = i_max_iter + + batch = self._compute( + batch, + x[batch_index_1:batch_index_2], + batch_labels, + mask_batch, + self.eps, + self.eps_step, + self._project, + self.num_random_init > 0 and i_max_iter == 0, + self._batch_id, + decay=self.decay, + momentum=momentum, + ) + + if rand_init_num == 0: + # initial (and possibly only) random restart: we only have this set of + # adversarial examples for now + adv_x[batch_index_1:batch_index_2] = copy.deepcopy(batch) + else: + # replace adversarial examples if they are successful + attack_success = compute_success_array( + self.estimator, # type: ignore + x[batch_index_1:batch_index_2], + targets[batch_index_1:batch_index_2], + batch, + self.targeted, + batch_size=self.batch_size, + ) + adv_x[batch_index_1:batch_index_2][attack_success] = batch[attack_success] + + logger.info( + "Success rate of attack: %.2f%%", + 100 + * compute_success( + self.estimator, # type: ignore + x, + targets, + adv_x, + self.targeted, + batch_size=self.batch_size, # type: ignore + ), + ) + else: + if self.num_random_init > 0: # pragma: no cover + raise ValueError("Random initialisation is only supported for classification.") + + # Set up targets + targets = self._set_targets(x, y, classifier_mixin=False) + + # Start to compute adversarial examples + if x.dtype == object: + adv_x = copy.deepcopy(x) + else: + adv_x = x.astype(ART_NUMPY_DTYPE) + + momentum = np.zeros(adv_x.shape) + + for i_max_iter in trange(self.max_iter, desc="PGD - Iterations", disable=not self.verbose): + self._i_max_iter = i_max_iter + + adv_x = self._compute( + adv_x, + x, + targets, + mask, + self.eps, + self.eps_step, + self._project, + self.num_random_init > 0 and i_max_iter == 0, + decay=self.decay, + momentum=momentum, + ) + + if self.summary_writer is not None: + self.summary_writer.reset() + + return adv_x diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 8a9d783101..4137bee651 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -108,7 +108,7 @@ def test_perturbation_equivalence(to_batch): from transformers import CLIPModel from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput - from art.attacks.evasion import ProjectedGradientDescentNumpy + from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy def attack_clip(): @@ -126,7 +126,7 @@ def attack_clip(): input_shape=(3, 224, 224), ) - attack = ProjectedGradientDescentNumpy( + attack = CLIPProjectedGradientDescentNumpy( art_classifier, max_iter=2, eps=np.ones((3, 224, 224)) * 0.3, # np.reshape(norm_bound_eps(), (3, 1, 1)), @@ -187,7 +187,7 @@ def test_equivalence(max_iter): from transformers import CLIPModel from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput - from art.attacks.evasion import ProjectedGradientDescent + from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy def attack_clip(): @@ -207,7 +207,7 @@ def attack_clip(): input_shape=(3, 224, 224), ) - attack = ProjectedGradientDescent( + attack = CLIPProjectedGradientDescentNumpy( art_classifier, max_iter=max_iter, # eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), From 75897509b001bfa43b591dfeb475786e7f387209 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 26 Oct 2023 18:21:30 +0100 Subject: [PATCH 14/46] restore orignal fgsm and pgd files Signed-off-by: GiulioZizzo --- art/attacks/evasion/fast_gradient.py | 14 ++++---------- .../projected_gradient_descent_numpy.py | 8 ++++---- .../projected_gradient_descent_numpy.py | 6 ++++-- 3 files changed, 12 insertions(+), 16 deletions(-) diff --git a/art/attacks/evasion/fast_gradient.py b/art/attacks/evasion/fast_gradient.py index b42288b130..85491c2630 100644 --- a/art/attacks/evasion/fast_gradient.py +++ b/art/attacks/evasion/fast_gradient.py @@ -23,7 +23,6 @@ """ from __future__ import absolute_import, division, print_function, unicode_literals -import copy import logging from typing import Optional, Union, TYPE_CHECKING @@ -33,7 +32,6 @@ from art.attacks.attack import EvasionAttack from art.estimators.estimator import BaseEstimator, LossGradientsMixin from art.estimators.classification.classifier import ClassifierMixin -from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput from art.utils import ( compute_success, get_labels_np_array, @@ -137,7 +135,7 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes). :return: An array holding the adversarial examples. """ - adv_x = copy.deepcopy(x) + adv_x = x.copy() # Compute perturbation with implicit batching for batch_id in range(int(np.ceil(adv_x.shape[0] / float(self.batch_size)))): @@ -482,12 +480,8 @@ def _apply_perturbation( if self.estimator.clip_values is not None: clip_min, clip_max = self.estimator.clip_values if x.dtype == object: - if isinstance(x, HuggingFaceMultiModalInput): - for i_obj in range(x.shape[0]): - x[i_obj] = np.clip(x[i_obj]["pixel_values"].cpu().detach().numpy(), clip_min, clip_max) - else: - for i_obj in range(x.shape[0]): - x[i_obj] = np.clip(x[i_obj], clip_min, clip_max) + for i_obj in range(x.shape[0]): + x[i_obj] = np.clip(x[i_obj], clip_min, clip_max) else: x = np.clip(x, clip_min, clip_max) @@ -520,7 +514,7 @@ def _compute( x_adv = np.clip(x_adv, clip_min, clip_max) else: if x.dtype == object: - x_adv = copy.deepcopy(x) + x_adv = x.copy() else: x_adv = x.astype(ART_NUMPY_DTYPE) diff --git a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index be2a7db5ec..1ecc8b31f1 100644 --- a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -24,7 +24,7 @@ | Paper link: https://arxiv.org/abs/1706.06083 """ from __future__ import absolute_import, division, print_function, unicode_literals -import copy + import logging from typing import Optional, Union, TYPE_CHECKING @@ -55,7 +55,7 @@ class ProjectedGradientDescentCommon(FastGradientMethod): | Paper link: https://arxiv.org/abs/1706.06083 """ - attack_params = FastGradientMethod.attack_params + ["max_iter", "random_eps", "verbose"] + attack_params = FastGradientMethod.attack_params + ["decay", "max_iter", "random_eps", "verbose"] _estimator_requirements = (BaseEstimator, LossGradientsMixin) def __init__( @@ -376,7 +376,7 @@ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> n if rand_init_num == 0: # initial (and possibly only) random restart: we only have this set of # adversarial examples for now - adv_x[batch_index_1:batch_index_2] = copy.deepcopy(batch) + adv_x[batch_index_1:batch_index_2] = np.copy(batch) else: # replace adversarial examples if they are successful attack_success = compute_success_array( @@ -410,7 +410,7 @@ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> n # Start to compute adversarial examples if x.dtype == object: - adv_x = copy.deepcopy(x) + adv_x = x.copy() else: adv_x = x.astype(ART_NUMPY_DTYPE) diff --git a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index c859acd933..39fb5800fa 100644 --- a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -16,14 +16,16 @@ from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import ( ProjectedGradientDescentNumpy, ) - +from art.experimental.attacks.evasion.fast_gradient import ( + FastGradientMethodCLIP, +) from art.summary_writer import SummaryWriter if TYPE_CHECKING: from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE, OBJECT_DETECTOR_TYPE -class CLIPProjectedGradientDescentNumpy(ProjectedGradientDescentNumpy): +class CLIPProjectedGradientDescentNumpy(ProjectedGradientDescentNumpy, FastGradientMethodCLIP): def __init__( self, estimator: Union["CLASSIFIER_LOSS_GRADIENTS_TYPE", "OBJECT_DETECTOR_TYPE"], From b6f9cf03845d1b9896809da3c6833ab12b46a341 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Wed, 1 Nov 2023 17:04:40 +0000 Subject: [PATCH 15/46] moving to experimental Signed-off-by: GiulioZizzo --- art/defences/trainer/adversarial_trainer.py | 2 +- art/experimental/attacks/__init__.py | 3 ++ art/experimental/attacks/evasion/__init__.py | 3 ++ .../attacks/evasion/fast_gradient.py | 35 +++++++++++++++--- .../projected_gradient_descent_numpy.py | 33 ++++++++++++++--- clip_dev.py | 36 +++++++++++++------ 6 files changed, 92 insertions(+), 20 deletions(-) diff --git a/art/defences/trainer/adversarial_trainer.py b/art/defences/trainer/adversarial_trainer.py index ad98e763b9..6da53b1efb 100644 --- a/art/defences/trainer/adversarial_trainer.py +++ b/art/defences/trainer/adversarial_trainer.py @@ -225,7 +225,7 @@ def fit( # pylint: disable=W0221 for _ in trange(nb_epochs, desc="Adversarial training epochs"): # Shuffle the examples - # np.random.shuffle(ind) + np.random.shuffle(ind) pbar = tqdm(range(nb_batches)) self._classifier.training_loss = [] # type: ignore self._classifier.training_accuracy = [] # type: ignore diff --git a/art/experimental/attacks/__init__.py b/art/experimental/attacks/__init__.py index f11bdc8228..3e4e61c280 100644 --- a/art/experimental/attacks/__init__.py +++ b/art/experimental/attacks/__init__.py @@ -1 +1,4 @@ +""" +This module contains the experimental attacks for ART +""" from art.experimental.attacks import evasion diff --git a/art/experimental/attacks/evasion/__init__.py b/art/experimental/attacks/evasion/__init__.py index cdfb5ef51c..c66585c8e8 100644 --- a/art/experimental/attacks/evasion/__init__.py +++ b/art/experimental/attacks/evasion/__init__.py @@ -1,3 +1,6 @@ +""" +This module contains the fgsm attack for the multimodal CLIP model +""" from art.experimental.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import ( CLIPProjectedGradientDescentNumpy, ) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index d48ecb77d6..dd77fcda28 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -1,8 +1,29 @@ -import numpy as np +# MIT License +# +# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2023 +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the +# Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +This module contains an experimental FGSM attack for multimodal models. +""" import copy +from typing import Optional, Union, TYPE_CHECKING + +import numpy as np from art.attacks.evasion.fast_gradient import FastGradientMethod -from typing import Optional, Union, TYPE_CHECKING from art.attacks.attack import EvasionAttack from art.estimators.estimator import BaseEstimator, LossGradientsMixin from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput @@ -11,15 +32,19 @@ from art.config import ART_NUMPY_DTYPE from art.utils import ( - compute_success, - get_labels_np_array, random_sphere, projection, - check_and_transform_label_format, ) +if TYPE_CHECKING: + from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE + class FastGradientMethodCLIP(FastGradientMethod): + """ + Implementation of the FGSM attack operating on the image portion of multimodal inputs + to the CLIP model. + """ attack_params = EvasionAttack.attack_params + [ "norm", diff --git a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index 39fb5800fa..597d6cecbb 100644 --- a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -1,6 +1,27 @@ +# MIT License +# +# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2023 +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the +# Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +This module contains an experimental PGD attack for multimodal models. +""" from __future__ import absolute_import, division, print_function, unicode_literals import copy +import logging from typing import Optional, Union, TYPE_CHECKING import numpy as np @@ -8,10 +29,7 @@ from art.config import ART_NUMPY_DTYPE from art.estimators.classification.classifier import ClassifierMixin -from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format, compute_success_array - -if TYPE_CHECKING: - from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE, OBJECT_DETECTOR_TYPE +from art.utils import compute_success, compute_success_array from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import ( ProjectedGradientDescentNumpy, @@ -24,8 +42,15 @@ if TYPE_CHECKING: from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE, OBJECT_DETECTOR_TYPE +logger = logging.getLogger(__name__) + class CLIPProjectedGradientDescentNumpy(ProjectedGradientDescentNumpy, FastGradientMethodCLIP): + """ + Implementation of the PGD attack operating on the image portion of multimodal inputs + to the CLIP model. + """ + def __init__( self, estimator: Union["CLASSIFIER_LOSS_GRADIENTS_TYPE", "OBJECT_DETECTOR_TYPE"], diff --git a/clip_dev.py b/clip_dev.py index e30e92fbed..08cc9785e4 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,6 +1,5 @@ import numpy as np -from art.estimators.hf_mm import HFMMPyTorch -from art.estimators.hf_mm import MultiModalHuggingFaceInput +from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput from art.attacks.evasion import ProjectedGradientDescent @@ -230,14 +229,23 @@ def test_predict(): def test_adv_train(): import torch - from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput - from art.defences.trainer import AdversarialTrainerMadryPGD + from transformers import CLIPProcessor, CLIPModel + from art.defences.trainer import AdversarialTrainer + from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput + from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy + + (x_train, y_train), (x_test, y_test) = get_cifar_data() model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels, num_classes = get_and_process_input() + inputs, original_image, _, num_classes = get_and_process_input() optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + + text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] + inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() + art_classifier = HFMMPyTorch( model, nb_classes=num_classes, @@ -246,12 +254,20 @@ def test_adv_train(): clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224), ) - trainer = AdversarialTrainerMadryPGD( - art_classifier, nb_epochs=10, eps=8 / 255, eps_step=1 / 255, max_iter=10, num_random_init=0 + + attack = CLIPProjectedGradientDescentNumpy( + art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) + + trainer = AdversarialTrainer( + art_classifier, attacks=attack, ) - inputs = MultiModalHuggingFaceInput(**inputs) + inputs = HuggingFaceMultiModalInput(**inputs) - trainer.fit(inputs, labels.detach().cpu().numpy()) + trainer.fit(inputs, y_train) test_adv_train() From 4356c08ff6e33c09e39ff96af32180670cfaecc6 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 7 Nov 2023 05:57:31 -0600 Subject: [PATCH 16/46] moving labels to correct device, remove repeated code Signed-off-by: GiulioZizzo --- .../huggingface_multimodal/huggingface_mm.py | 1 + .../huggingface_mm_inputs.py | 10 ++----- clip_dev.py | 29 +++++++++++-------- 3 files changed, 20 insertions(+), 20 deletions(-) diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py index 2dbb091a31..8593a6dcc5 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py @@ -308,6 +308,7 @@ def fit( # pylint: disable=W0221 y_batch = y_tensor[ind[batch_size * m : batch_size * (m + 1)]] x_batch = x_batch.to(self._device) + y_batch = y_batch.to(self._device) # Zero the parameter gradients self._optimizer.zero_grad() diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py index 458c4fff63..f615c99ce8 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py @@ -85,16 +85,10 @@ def __setitem__(self, key, value): f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" ) - def __getitem__(self, item: Union[slice, Tuple, int, str]) -> Union[HuggingFaceMultiModalInput, "torch.Tensor"]: + def __getitem__(self, item: Union[slice, Tuple, int, str, np.ndarray]) -> Union[HuggingFaceMultiModalInput, "torch.Tensor"]: # print('__getitem__ key ', item) # print('with type ', type(item)) - if isinstance(item, (slice, tuple)): - pixel_values = UserDict.__getitem__(self, "pixel_values") - pixel_values = pixel_values[item] - output = HuggingFaceMultiModalInput(**self) - output["pixel_values"] = pixel_values - return output - if isinstance(item, int): + if isinstance(item, (slice, tuple, int)): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] output = HuggingFaceMultiModalInput(**self) diff --git a/clip_dev.py b/clip_dev.py index 08cc9785e4..5868118d43 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -12,7 +12,7 @@ MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) - +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') def get_and_process_input(to_one_hot=False, return_batch=False): @@ -73,7 +73,7 @@ def get_cifar_data(): x_train = x_train / 255.0 x_test = x_test / 255.0 - return (x_train[0:100], y_train[0:100]), (x_test[0:100], y_test[0:100]) + return (x_train[0:250], y_train[0:250]), (x_test[0:250], y_test[0:250]) def attack_clip_pgd(): @@ -137,10 +137,11 @@ def cifar_clip_pgd(): import requests from transformers import CLIPProcessor, CLIPModel - + from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput + from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] - labels = torch.tensor(np.asarray([0, 1, 3])) + labels = torch.tensor(np.asarray([0, 1, 3, 4])) input_list = [] for fname in ["000000039769.jpg", "000000000285.jpg", "000000002006.jpg", "000000002149.jpg"]: @@ -159,14 +160,16 @@ def cifar_clip_pgd(): original_images = np.concatenate(original_images) art_classifier = HFMMPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), input_shape=(3, 224, 224) + model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), input_shape=(3, 224, 224), + nb_classes=5, ) - my_input = MultiModalHuggingFaceInput(**inputs) + my_input = HuggingFaceMultiModalInput(**inputs) clean_preds = art_classifier.predict(my_input) print(clean_preds) - - attack = ProjectedGradientDescent( + clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) + print('clean acc ', clean_acc) + attack = CLIPProjectedGradientDescentNumpy( art_classifier, max_iter=10, eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), @@ -174,6 +177,8 @@ def cifar_clip_pgd(): ) x_adv = attack.generate(my_input, labels) adv_preds = art_classifier.predict(x_adv) + adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) + print('adv_acc ', adv_acc) print(clean_preds) print(adv_preds) @@ -247,7 +252,7 @@ def test_adv_train(): original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() art_classifier = HFMMPyTorch( - model, + model.to(device), nb_classes=num_classes, optimizer=optimizer, loss=torch.nn.CrossEntropyLoss(), @@ -263,15 +268,15 @@ def test_adv_train(): ) trainer = AdversarialTrainer( - art_classifier, attacks=attack, + art_classifier, attacks=attack, ratio=1.0, ) inputs = HuggingFaceMultiModalInput(**inputs) trainer.fit(inputs, y_train) -test_adv_train() +# test_adv_train() # test_predict() # test_fit() # attack_clip_pgd() -# cifar_clip_pgd() +cifar_clip_pgd() From 95b6629b82fd7147b23933891174189f2064c8ff Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 7 Nov 2023 07:46:01 -0600 Subject: [PATCH 17/46] update notebook and formatting edits Signed-off-by: GiulioZizzo --- art/defences/trainer/adversarial_trainer.py | 1 - .../huggingface_multimodal/huggingface_mm.py | 20 +++-- .../huggingface_mm_inputs.py | 10 ++- clip_dev.py | 24 +++--- notebooks/clip_attack.ipynb | 73 ++++++++++--------- .../attacks/evasion/test_multimodal_attack.py | 29 +------- 6 files changed, 79 insertions(+), 78 deletions(-) diff --git a/art/defences/trainer/adversarial_trainer.py b/art/defences/trainer/adversarial_trainer.py index 6da53b1efb..93df345012 100644 --- a/art/defences/trainer/adversarial_trainer.py +++ b/art/defences/trainer/adversarial_trainer.py @@ -205,7 +205,6 @@ def fit( # pylint: disable=W0221 nb_batches = int(np.ceil(len(x) / batch_size)) ind = np.arange(len(x)) attack_id = 0 - from tqdm import tqdm # Precompute adversarial samples for transferred attacks logged = False diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py index 8593a6dcc5..381b86ac2c 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py @@ -53,7 +53,6 @@ def __init__( model: "transformers.PreTrainedModel", loss: "torch.nn.modules.loss._Loss", input_shape: Tuple[int, ...], - nb_classes: int, optimizer: Optional["torch.optim.Optimizer"] = None, clip_values: Optional["CLIP_VALUES_TYPE"] = None, channels_first: Optional[bool] = True, @@ -68,7 +67,6 @@ def __init__( :param model: CLIP model :param input_shape: The shape of one input sample. :param optimizer: The optimizer for training the classifier. - :param nb_classes: ... :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus @@ -97,7 +95,6 @@ def __init__( self._input_shape = input_shape self._optimizer = optimizer self.loss_fn = loss - self.nb_classes = nb_classes if self.postprocessing_defences is not None: raise ValueError("This estimator does not support `postprocessing_defences`.") self._model = model @@ -244,7 +241,7 @@ def predict( :param x: Dictionary inputs for the CLIP model. :param batch_size: Batch size. - :return: + :return: Predictions over the supplied data. """ from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput @@ -278,7 +275,20 @@ def fit( # pylint: disable=W0221 **kwargs, ) -> None: """ - Fit the classifier on the training set + Fit the classifier on the training set `(x, y)`. + + :param x: Training data. + :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of + shape (nb_samples,). + :param batch_size: Size of batches. + :param nb_epochs: Number of epochs to use for training. + :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode. + :param drop_last: Set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by + the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then + the last batch will be smaller. (default: ``False``) + :param scheduler: Learning rate scheduler to run at the start of every epoch. + :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch + and providing it takes no effect. """ import torch from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py index f615c99ce8..24a6c29e24 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py @@ -22,7 +22,7 @@ from __future__ import annotations from collections import UserDict -from typing import List, Tuple, Union, TYPE_CHECKING +from typing import Optional, Tuple, Union, TYPE_CHECKING import numpy as np @@ -40,8 +40,8 @@ class HuggingFaceMultiModalInput(UserDict): """ dtype = object - shape = (1, 3, 224, 224) - ndim = 4 + shape: Optional[Tuple] = None + ndim: Optional[int] = None def __setitem__(self, key, value): import torch @@ -85,7 +85,9 @@ def __setitem__(self, key, value): f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" ) - def __getitem__(self, item: Union[slice, Tuple, int, str, np.ndarray]) -> Union[HuggingFaceMultiModalInput, "torch.Tensor"]: + def __getitem__( + self, item: Union[slice, Tuple, int, str, np.ndarray] + ) -> Union[HuggingFaceMultiModalInput, "torch.Tensor"]: # print('__getitem__ key ', item) # print('with type ', type(item)) if isinstance(item, (slice, tuple, int)): diff --git a/clip_dev.py b/clip_dev.py index 5868118d43..22ce8f84e8 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,6 +1,5 @@ import numpy as np -from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput - +from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, MultiModalHuggingFaceInput from art.attacks.evasion import ProjectedGradientDescent import torch @@ -12,7 +11,8 @@ MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + def get_and_process_input(to_one_hot=False, return_batch=False): @@ -81,6 +81,7 @@ def attack_clip_pgd(): import requests from transformers import CLIPProcessor, CLIPModel + from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() @@ -105,7 +106,7 @@ def attack_clip_pgd(): model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) - my_input = MultiModalHuggingFaceInput(**inputs) + my_input = HuggingFaceMultiModalInput(**inputs) labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) # loss = art_classifier._get_losses(my_input, labels) @@ -139,6 +140,7 @@ def cifar_clip_pgd(): from transformers import CLIPProcessor, CLIPModel from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy + text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] labels = torch.tensor(np.asarray([0, 1, 3, 4])) @@ -160,7 +162,10 @@ def cifar_clip_pgd(): original_images = np.concatenate(original_images) art_classifier = HFMMPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), input_shape=(3, 224, 224), + model, + loss=loss_fn, + clip_values=(np.min(original_images), np.max(original_images)), + input_shape=(3, 224, 224), nb_classes=5, ) @@ -168,7 +173,7 @@ def cifar_clip_pgd(): clean_preds = art_classifier.predict(my_input) print(clean_preds) clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - print('clean acc ', clean_acc) + print("clean acc ", clean_acc) attack = CLIPProjectedGradientDescentNumpy( art_classifier, max_iter=10, @@ -178,7 +183,7 @@ def cifar_clip_pgd(): x_adv = attack.generate(my_input, labels) adv_preds = art_classifier.predict(x_adv) adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - print('adv_acc ', adv_acc) + print("adv_acc ", adv_acc) print(clean_preds) print(adv_preds) @@ -214,7 +219,6 @@ def test_fit(): def test_predict(): import torch from transformers import CLIPModel - from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels, num_classes = get_and_process_input() @@ -268,7 +272,9 @@ def test_adv_train(): ) trainer = AdversarialTrainer( - art_classifier, attacks=attack, ratio=1.0, + art_classifier, + attacks=attack, + ratio=1.0, ) inputs = HuggingFaceMultiModalInput(**inputs) diff --git a/notebooks/clip_attack.ipynb b/notebooks/clip_attack.ipynb index b08ce360d1..161967fb8b 100644 --- a/notebooks/clip_attack.ipynb +++ b/notebooks/clip_attack.ipynb @@ -5,9 +5,11 @@ "id": "4e111634-8795-4707-b71e-9eb2df0eba78", "metadata": {}, "source": [ - "# Attacking CLIP for image classification\n", + "

Attacking CLIP for Image Classification

\n", + "In this notebook we show how to use the experimental tools in ART to attack the CLIP model.\n", "\n", - "In this notebook we show how to use the experimental tools in ART to attack the CLIP model.\n" + "CLIP is a multimodal foundation model able to handle both images and text.\n", + "Here we deomstrate how to attack the image recognition portion of CLIP so that it miscassifies a given input.\n" ] }, { @@ -15,25 +17,16 @@ "execution_count": 1, "id": "9da58be9-e228-4928-9f73-d146cbb3cc7a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/giulio.zizzo1/art_clip_17/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "import torch\n", + "sys.path.append('../')\n", + "from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput\n", + "from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy\n", "\n", - "from art.estimators.hf_mm import HFMMPyTorch, MultiModalHuggingFaceInput\n", - "from art.attacks.evasion import ProjectedGradientDescent\n", - "\n", - "\n", + "# Image normalization numbers\n", "MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073])\n", "STD = np.asarray([0.26862954, 0.26130258, 0.27577711])" ] @@ -47,7 +40,7 @@ "source": [ "def get_data():\n", " \"\"\"\n", - " We get sample data from the coco dataset.\n", + " We get sample data from the COCO dataset.\n", " \"\"\"\n", " from PIL import Image\n", " import requests\n", @@ -130,12 +123,13 @@ " input_shape=(3, 224, 224)\n", " )\n", "\n", - " art_input = MultiModalHuggingFaceInput(**inputs)\n", + " art_input = HuggingFaceMultiModalInput(**inputs)\n", " clean_preds = art_classifier.predict(art_input)\n", + "\n", " clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels)\n", " print('The clean accuracy is ', clean_acc)\n", "\n", - " attack = ProjectedGradientDescent(\n", + " attack = CLIPProjectedGradientDescentNumpy(\n", " art_classifier,\n", " max_iter=10,\n", " eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)),\n", @@ -145,12 +139,12 @@ " adv_preds = art_classifier.predict(x_adv)\n", " adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels)\n", "\n", - " print('The adversarial accuracy is ', clean_acc)\n" + " print('The adversarial accuracy is ', adv_acc)\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "5f011a60-2381-4d3f-866a-a39ae2279dde", "metadata": {}, "outputs": [ @@ -158,14 +152,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-10-17 06:11:36.199655: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2023-10-17 06:11:36.232269: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2023-10-17 06:11:36.232299: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2023-10-17 06:11:36.232327: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-10-17 06:11:36.240857: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-17 06:11:37.143845: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.38it/s]\n" + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 38.93it/s]" ] }, { @@ -179,15 +166,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "PGD - Iterations: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 10.61it/s]\n", - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 39.43it/s]" + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eefe09b660a046c6a2fb3a24ca5104f3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "PGD - Iterations: 0%| | 0/10 [00:00= np.min(original_image)) assert np.all(manual_adv <= np.max(original_image)) - # eps = norm_bound_eps() eps_mins = original_image - 0.3 eps_maxs = original_image + 0.3 @@ -329,7 +307,6 @@ def test_predict(): art_classifier = HFMMPyTorch( model, - nb_classes=num_classes, loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224), From 45616bd4e2069a3c07846284418bf6b2a6f5e8f0 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 7 Nov 2023 15:31:57 -0600 Subject: [PATCH 18/46] update tests Signed-off-by: GiulioZizzo --- clip_dev.py | 2 +- .../attacks/evasion/test_multimodal_attack.py | 168 ++++++------------ 2 files changed, 57 insertions(+), 113 deletions(-) diff --git a/clip_dev.py b/clip_dev.py index 22ce8f84e8..f5113ba81d 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,5 +1,5 @@ import numpy as np -from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, MultiModalHuggingFaceInput +from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput from art.attacks.evasion import ProjectedGradientDescent import torch diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index b12d16064f..12f8672142 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -160,140 +160,84 @@ def manual_attack(): assert np.allclose(manual_sample, current_x) -@pytest.mark.only_with_platform("huggingface") -@pytest.mark.parametrize("max_iter", [1, 5]) -def test_equivalence(max_iter): - """ - Test that the result from using ART tools matches that obtained by manual calculation. - """ - import torch +def test_attack_functionality(): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + import torch + import requests + from PIL import Image - from transformers import CLIPModel + from transformers import CLIPProcessor, CLIPModel from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - def attack_clip(): - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - loss_fn = torch.nn.CrossEntropyLoss() - - inputs, original_image, labels, num_classes = get_and_process_input() - original_image = inputs.pixel_values.clone().cpu().numpy() - - my_input = HuggingFaceMultiModalInput(**inputs) - - art_classifier = HFMMPyTorch( - model, - loss=loss_fn, - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=max_iter, - eps=np.ones((3, 224, 224)) * 0.3, - eps_step=np.ones((3, 224, 224)) * 0.1, - targeted=False, - num_random_init=0, - ) - - x_adv = attack.generate(my_input, labels) - x_adv = x_adv[0] - check_vals = torch.reshape(x_adv["pixel_values"], (-1,)) - - assert torch.all(torch.ge(check_vals, np.min(original_image))) - assert torch.all(torch.le(check_vals, np.max(original_image))) - - eps_mins = torch.tensor(original_image - 0.3).float() - eps_maxs = torch.tensor(original_image + 0.3).float() - eps_mins = torch.reshape(eps_mins, (-1,)) - eps_maxs = torch.reshape(eps_maxs, (-1,)) + std = np.asarray([0.26862954, 0.26130258, 0.27577711]) - assert torch.all(torch.ge(check_vals, eps_mins)) - assert torch.all(torch.le(check_vals, eps_maxs)) + def norm_bound_eps(eps_bound=None): + if eps_bound is None: + eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) + eps_bound = np.abs(eps_bound / std) + return eps_bound - return x_adv + text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] - def manual_attack(): + labels = torch.tensor(np.asarray([0, 1, 3, 4])) - lossfn = torch.nn.CrossEntropyLoss() + input_list = [] + for fname in ["000000039769.jpg", "000000000285.jpg", "000000002006.jpg", "000000002149.jpg"]: + url = "http://images.cocodataset.org/val2017/" + fname + input_list.append(Image.open(requests.get(url, stream=True).raw)) - adv_current = None - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - model = model.to(device) - - for i in range(max_iter): - - inputs, original_image, labels, _ = get_and_process_input() - inputs = inputs.to(device) - - eps_mins = torch.tensor(original_image - 0.3).float().to(device) - eps_maxs = torch.tensor(original_image + 0.3).float().to(device) - - init_max = torch.max(inputs["pixel_values"]).to(device) - init_min = torch.min(inputs["pixel_values"]).to(device) - - if adv_current is not None: - inputs["pixel_values"] = torch.tensor(adv_current).to(device) - inputs["pixel_values"].requires_grad_(True) - - outputs = model(**inputs) - - loss = lossfn(outputs.logits_per_image, labels.to(device)) - loss.backward() - - sign = torch.sign(inputs["pixel_values"].grad) - pixel_values = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) - pixel_values = torch.clamp(pixel_values, min=eps_mins, max=eps_maxs) - - model.zero_grad() - - adv_current = pixel_values.cpu().detach().numpy() + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - return adv_current + loss_fn = torch.nn.CrossEntropyLoss() + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_images = [] + for i in range(4): + original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) - inputs, original_image, labels, num_classes = get_and_process_input() - manual_adv = manual_attack() - art_adv = attack_clip() + original_images = np.stack(original_images) - art_adv = art_adv["pixel_values"] - art_adv = art_adv.cpu().detach().numpy() + art_classifier = HFMMPyTorch( + model, + loss=loss_fn, + clip_values=(np.min(original_images), np.max(original_images)), + input_shape=(3, 224, 224), + ) - art_adv = art_adv.flatten() - original_image = original_image.flatten() - manual_adv = manual_adv.flatten() + my_input = HuggingFaceMultiModalInput(**inputs) + clean_preds = art_classifier.predict(my_input) + clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - """ - Assert valid adversarial examples - """ - assert np.all(art_adv >= np.min(original_image)) - assert np.all(art_adv <= np.max(original_image)) - assert np.all(manual_adv >= np.min(original_image)) - assert np.all(manual_adv <= np.max(original_image)) + attack = CLIPProjectedGradientDescentNumpy( + art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) + x_adv = attack.generate(my_input, labels) + adv_preds = art_classifier.predict(x_adv) + adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - eps_mins = original_image - 0.3 - eps_maxs = original_image + 0.3 + x_adv = x_adv["pixel_values"] + x_adv = x_adv.cpu().detach().numpy() - assert np.all(art_adv >= eps_mins) - assert np.all(art_adv <= eps_maxs) - assert np.all(manual_adv >= eps_mins) - assert np.all(manual_adv <= eps_maxs) + assert np.all(x_adv >= np.min(original_images)) + assert np.all(x_adv <= np.max(original_images)) - art_delta = art_adv - original_image - target = manual_adv - original_image - # np.save('art_adv_' + str(max_iter) + '.npy', art_delta) - # target = np.load('art_adv_' + str(max_iter) + '.npy') + eps_mins = original_images - np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)) + eps_maxs = original_images + np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)) - assert np.allclose(art_delta, target, rtol=1e-04, atol=1e-04) + eps_mins = eps_mins.flatten() + eps_maxs = eps_maxs.flatten() + x_adv = x_adv.flatten() + assert np.all(np.logical_or(x_adv >= eps_mins, np.isclose(x_adv, eps_mins))) + assert np.all(np.logical_or(x_adv <= eps_maxs, np.isclose(x_adv, eps_maxs))) -""" -TODO: move some of the fits to more appropriate testing files -""" + assert clean_acc == 1.0 + assert adv_acc == 0.0 @pytest.mark.only_with_platform("huggingface") From 469f6bf356eacca560d908eb582b43e094b8549c Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Mon, 27 Nov 2023 16:15:39 +0000 Subject: [PATCH 19/46] adding comments to mm_inputs Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 2 +- .github/workflows/ci-style-checks.yml | 2 +- .../huggingface_multimodal/huggingface_mm.py | 4 - .../huggingface_mm_inputs.py | 74 ++++++++++++------- 4 files changed, 51 insertions(+), 31 deletions(-) diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index c9803c6ec9..5cbd57ba70 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -16,7 +16,7 @@ on: branches: - main - dev* - - clip_checking + - clip_1.17_dev # Run scheduled CI flow daily schedule: diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml index 80e2529a0b..499a18cb5d 100644 --- a/.github/workflows/ci-style-checks.yml +++ b/.github/workflows/ci-style-checks.yml @@ -16,7 +16,7 @@ on: branches: - main - dev* - - clip_checking + - clip_1.17_dev # Run scheduled CI flow daily diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py index 381b86ac2c..e8d64b93f9 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py @@ -282,10 +282,6 @@ def fit( # pylint: disable=W0221 shape (nb_samples,). :param batch_size: Size of batches. :param nb_epochs: Number of epochs to use for training. - :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode. - :param drop_last: Set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by - the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then - the last batch will be smaller. (default: ``False``) :param scheduler: Learning rate scheduler to run at the start of every epoch. :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch and providing it takes no effect. diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py index 24a6c29e24..75f4047a8b 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py +++ b/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py @@ -44,13 +44,15 @@ class HuggingFaceMultiModalInput(UserDict): ndim: Optional[int] = None def __setitem__(self, key, value): + """ + Allows setting key-value paris for HuggingFaceMultiModalInput + :param key: Either a slice, int, or numpy array for array like operations + or a string for traditional dictionary setting. + :param value: Value to set, pixel_values are required for array like operations. + :return: None + """ import torch - # if not isinstance(value, (torch.Tensor, np.ndarray, )): - # print(type(value)) - # raise ValueError("Supplied values must be pytorch tensors or numpy arrays") - # print('key ', key) - # print('value ', value) if isinstance(key, slice): pixel_values = UserDict.__getitem__(self, "pixel_values") original_shape = pixel_values.shape @@ -82,21 +84,19 @@ def __setitem__(self, key, value): super().__setitem__("pixel_values", pixel_values) else: raise ValueError( - f"Unsupported key {key} with type {type(key)}, " f"value {value} for __setitem__ in ARTInput" + f"Unsupported key {key} with type {type(key)}, " + f"value {value} for __setitem__ in HuggingFaceMultiModalInput" ) def __getitem__( self, item: Union[slice, Tuple, int, str, np.ndarray] ) -> Union[HuggingFaceMultiModalInput, "torch.Tensor"]: - # print('__getitem__ key ', item) - # print('with type ', type(item)) - if isinstance(item, (slice, tuple, int)): - pixel_values = UserDict.__getitem__(self, "pixel_values") - pixel_values = pixel_values[item] - output = HuggingFaceMultiModalInput(**self) - output["pixel_values"] = pixel_values - return output - if isinstance(item, np.ndarray): + """ + Get value from HuggingFaceMultiModalInput + :param item: Item to get. If accessing via array like functionality (slice, int, etc) pixel_values are fetched. + Else, if passing a string will fetch like a ordinary dictionary + """ + if isinstance(item, (slice, tuple, int, np.ndarray)): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] output = HuggingFaceMultiModalInput(**self) @@ -104,9 +104,16 @@ def __getitem__( return output if item in self.keys(): return UserDict.__getitem__(self, item) - raise ValueError("Unsupported item for __getitem__ in ARTInput") + raise ValueError("Unsupported item for __getitem__ in HuggingFaceMultiModalInput") def __add__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> HuggingFaceMultiModalInput: + """ + Performs addition between either two instances of HuggingFaceMultiModalInput on the pixel_values or + adds a numpy array to the pixel_values if addition is performed between a HuggingFaceMultiModalInput + and a numpy array. + :param other: Other value which is being added to self. + :return: HuggingFaceMultiModalInput with updated "pixel_values". + """ import torch pixel_values = UserDict.__getitem__(self, "pixel_values") @@ -128,40 +135,57 @@ def __add__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> Huggi return output def __sub__(self, other: HuggingFaceMultiModalInput) -> HuggingFaceMultiModalInput: + """ + Performs subtraction between two instances of HuggingFaceMultiModalInput on the pixel_values. + + :param other: Other value which is being subtracted from self. + :return: HuggingFaceMultiModalInput with updated "pixel_values". + """ if isinstance(other, HuggingFaceMultiModalInput): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values - other["pixel_values"] output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values else: - raise ValueError("Unsupported type for __sub__ in ARTInput") + raise ValueError("Unsupported type for __sub__ in HuggingFaceMultiModalInput") return output def __mul__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> HuggingFaceMultiModalInput: + """ + Performs multiplication between either two instances of HuggingFaceMultiModalInput on the pixel_values or + adds a numpy array to the pixel_values if addition is performed between a HuggingFaceMultiModalInput + and a numpy array. + :param other: Other value which is being multiplied with self. + :return: HuggingFaceMultiModalInput with updated "pixel_values". + """ import torch + pixel_values = UserDict.__getitem__(self, "pixel_values") + if isinstance(other, HuggingFaceMultiModalInput): - pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values * other["pixel_values"] elif isinstance(other, np.ndarray): - pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values * torch.tensor(other) else: - raise ValueError("Unsupported type for __mul__ in ARTInput") + raise ValueError("Unsupported type for __mul__ in HuggingFaceMultiModalInput") output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output - def __len__(self): + def __len__(self) -> int: + """ + Fetched the length of the pixel_values + :return: length of the pixel_values tensor + """ pixel_values = UserDict.__getitem__(self, "pixel_values") return len(pixel_values) def reshape(self, new_shape: Tuple) -> HuggingFaceMultiModalInput: """ - Defines reshaping on the HF input. + Defines reshaping on the HuggingFaceMultiModalInput input. :param new_shape: The new shape for the input - :return: A ARTInput instance with the pixel values having their shape updated. + :return: A HuggingFaceMultiModalInput instance with the pixel values having their shape updated. """ import torch @@ -187,11 +211,11 @@ def is_leaf(): """ Enable mypy compatibility """ - raise ValueError("Trying to acces is_leaf for the whole dictionay. Please use on individual tensors") + raise ValueError("Trying to access is_leaf for the whole dictionary. Please use on individual tensors") @staticmethod def grad(): """ Enable mypy compatibility """ - raise ValueError("Trying to acces is_leaf for the whole dictionay. Please use on individual tensors") + raise ValueError("Trying to access is_leaf for the whole dictionary. Please use on individual tensors") From 70f01cbec0f3cd0031578e3082e347a55a793b91 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Mon, 27 Nov 2023 16:55:22 +0000 Subject: [PATCH 20/46] remove old files and redundant changes Signed-off-by: GiulioZizzo --- .../projected_gradient_descent_numpy.py | 2 +- art/defences/trainer/adversarial_trainer.py | 15 +--- art_adv_1.npy | Bin 602240 -> 0 bytes art_adv_5.npy | Bin 602240 -> 0 bytes clip_initial_attack.py | 85 ------------------ 5 files changed, 3 insertions(+), 99 deletions(-) delete mode 100644 art_adv_1.npy delete mode 100644 art_adv_5.npy delete mode 100644 clip_initial_attack.py diff --git a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index 1ecc8b31f1..bcb9c0686e 100644 --- a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -55,7 +55,7 @@ class ProjectedGradientDescentCommon(FastGradientMethod): | Paper link: https://arxiv.org/abs/1706.06083 """ - attack_params = FastGradientMethod.attack_params + ["decay", "max_iter", "random_eps", "verbose"] + attack_params = FastGradientMethod.attack_params + ["max_iter", "random_eps", "verbose"] _estimator_requirements = (BaseEstimator, LossGradientsMixin) def __init__( diff --git a/art/defences/trainer/adversarial_trainer.py b/art/defences/trainer/adversarial_trainer.py index 93df345012..477537d860 100644 --- a/art/defences/trainer/adversarial_trainer.py +++ b/art/defences/trainer/adversarial_trainer.py @@ -225,11 +225,8 @@ def fit( # pylint: disable=W0221 for _ in trange(nb_epochs, desc="Adversarial training epochs"): # Shuffle the examples np.random.shuffle(ind) - pbar = tqdm(range(nb_batches)) - self._classifier.training_loss = [] # type: ignore - self._classifier.training_accuracy = [] # type: ignore - for batch_id in pbar: + for batch_id in range(nb_batches): # Create batch data x_batch = x[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]].copy() y_batch = y[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]] @@ -258,16 +255,8 @@ def fit( # pylint: disable=W0221 x_batch[adv_ids] = x_adv # Fit batch - self._classifier.fit( - x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=False, **kwargs - ) - pbar.set_description( - f"Loss {np.mean(np.stack(self._classifier.training_loss)):.2f} " # type: ignore - f"Acc {np.mean(self._classifier.training_accuracy):.2f} " # type: ignore - ) + self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=0, **kwargs) attack_id = (attack_id + 1) % len(self.attacks) - # torch.save(self._classifier.model.state_dict(), 'clip_adv_trained_' + str(epoch) + '.pt') - # 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zi|2494~Enar;hr3dL|dgh8cks>p92?mjC8GzjEHWmY09*)N793iTcD|@8^g!+I7xb zKXAkKzPsAE#ag4fv$ek1>EV0$%rNJ3?fU4>l)Zgs7S?hIOxMNp4?h0JMklv1ah&y6 zPdxh+W0S_J>-*%8b2!7j*u3Sj^~?XGEbdD^HBMdoQp5Sj#!rg5`1+jlGx$9xcg9)a z$iK({Qr^CAF;d>@=Hnhae7%pj*f_-7`}ng@YZ!a0 zc2Ae5vufS0vsWD3Lu%@S@Y6VV57`s`D{=|n>U{S;+goRbW4SWN!N2csu-Ow1eRthG zblsV^ANymAe`>uK_cz^q@$Q4>-*@xwxp98db+KJn&tA+gxY6OSE@rfu7ZV$1brh@@OPh$jm8m~*lY7y6LqGq+*NN*%zE}0yYJ9iUvx!1wXT18 z+Y7uor51bpjQ<%f{N)kD*Z$@4HOG8#OrCf4a Date: Mon, 27 Nov 2023 17:16:36 +0000 Subject: [PATCH 21/46] moving functionality to experimental Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 79 +++++++++++++++++-- art/utils.py | 13 +-- clip_dev.py | 11 +-- 3 files changed, 78 insertions(+), 25 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index dd77fcda28..ef12f9d3cb 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -19,6 +19,7 @@ This module contains an experimental FGSM attack for multimodal models. """ import copy +from collections import UserDict from typing import Optional, Union, TYPE_CHECKING import numpy as np @@ -31,15 +32,77 @@ from art.summary_writer import SummaryWriter from art.config import ART_NUMPY_DTYPE -from art.utils import ( - random_sphere, - projection, -) +from art.utils import random_sphere, projection_l1_1, projection_l1_2 if TYPE_CHECKING: from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE +def multimodal_projection( + values: np.ndarray, eps: Union[int, float, np.ndarray], norm_p: Union[int, float, str] +) -> np.ndarray: + """ + Experimental extension of the projection in art.utils to support multimodal inputs. + + Project `values` on the L_p norm ball of size `eps`. + + :param values: Array of perturbations to clip. + :param eps: Maximum norm allowed. + :param norm_p: L_p norm to use for clipping. + Only 1, 2 , `np.Inf` 1.1 and 1.2 supported for now. + 1.1 and 1.2 compute orthogonal projections on l1-ball, using two different algorithms + :return: Values of `values` after projection. + """ + # Pick a small scalar to avoid division by 0 + tol = 10e-8 + values_tmp = values.reshape((values.shape[0], -1)) + + if norm_p == 2: + if isinstance(eps, np.ndarray): + raise NotImplementedError("The parameter `eps` of type `np.ndarray` is not supported to use with norm 2.") + + values_tmp = values_tmp * np.expand_dims( + np.minimum(1.0, eps / (np.linalg.norm(values_tmp, axis=1) + tol)), axis=1 + ) + + elif norm_p == 1: + if isinstance(eps, np.ndarray): + raise NotImplementedError("The parameter `eps` of type `np.ndarray` is not supported to use with norm 1.") + + values_tmp = values_tmp * np.expand_dims( + np.minimum(1.0, eps / (np.linalg.norm(values_tmp, axis=1, ord=1) + tol)), + axis=1, + ) + elif norm_p == 1.1: + values_tmp = projection_l1_1(values_tmp, eps) + elif norm_p == 1.2: + values_tmp = projection_l1_2(values_tmp, eps) + + elif norm_p in [np.inf, "inf"]: + if isinstance(eps, np.ndarray): + if isinstance(values_tmp, UserDict): + eps = eps * np.ones_like(values["pixel_values"].cpu().detach().numpy()) + else: + eps = eps * np.ones_like(values) + eps = eps.reshape([eps.shape[0], -1]) # type: ignore + + if isinstance(values_tmp, UserDict): + sign = np.sign(values_tmp["pixel_values"].cpu().detach().numpy()) + mag = abs(values_tmp["pixel_values"].cpu().detach().numpy()) + values_tmp["pixel_values"] = sign * np.minimum(mag, eps) + else: + values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) + + else: + raise NotImplementedError( + 'Values of `norm_p` different from 1, 2, `np.inf` and "inf" are currently not ' "supported." + ) + + values = values_tmp.reshape(values.shape) + + return values + + class FastGradientMethodCLIP(FastGradientMethod): """ Implementation of the FGSM attack operating on the image portion of multimodal inputs @@ -264,17 +327,19 @@ def _compute( if x_adv.dtype == object: for i_sample in range(batch_index_1, batch_index_2): if isinstance(batch_eps, np.ndarray) and batch_eps.shape[0] == x_adv.shape[0]: - perturbation = projection( + perturbation = multimodal_projection( x_adv[i_sample] - x_init[i_sample], batch_eps[i_sample], self.norm ) else: - perturbation = projection(x_adv[i_sample] - x_init[i_sample], batch_eps, self.norm) + perturbation = multimodal_projection( + x_adv[i_sample] - x_init[i_sample], batch_eps, self.norm + ) x_adv[i_sample] = x_init[i_sample] + perturbation else: - perturbation = projection( + perturbation = multimodal_projection( x_adv[batch_index_1:batch_index_2] - x_init[batch_index_1:batch_index_2], batch_eps, self.norm ) x_adv[batch_index_1:batch_index_2] = x_init[batch_index_1:batch_index_2] + perturbation diff --git a/art/utils.py b/art/utils.py index 3504bd23be..b8e13d5fae 100644 --- a/art/utils.py +++ b/art/utils.py @@ -29,7 +29,6 @@ import tarfile import warnings import zipfile -from collections import UserDict from functools import wraps from inspect import signature from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union @@ -558,18 +557,10 @@ def projection(values: np.ndarray, eps: Union[int, float, np.ndarray], norm_p: U elif norm_p in [np.inf, "inf"]: if isinstance(eps, np.ndarray): - if isinstance(values_tmp, UserDict): - eps = eps * np.ones_like(values["pixel_values"].cpu().detach().numpy()) - else: - eps = eps * np.ones_like(values) + eps = eps * np.ones_like(values) eps = eps.reshape([eps.shape[0], -1]) # type: ignore - if isinstance(values_tmp, UserDict): - sign = np.sign(values_tmp["pixel_values"].cpu().detach().numpy()) - mag = abs(values_tmp["pixel_values"].cpu().detach().numpy()) - values_tmp["pixel_values"] = sign * np.minimum(mag, eps) - else: - values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) + values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps) else: raise NotImplementedError( diff --git a/clip_dev.py b/clip_dev.py index f5113ba81d..01fb53056a 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -81,7 +81,6 @@ def attack_clip_pgd(): import requests from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") loss_fn = torch.nn.CrossEntropyLoss() @@ -138,7 +137,6 @@ def cifar_clip_pgd(): import requests from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] @@ -192,7 +190,7 @@ def cifar_clip_pgd(): def test_fit(): from transformers import CLIPProcessor, CLIPModel - (x_train, y_train), (x_test, y_test) = get_cifar_data() + (x_train, y_train), (_, _) = get_cifar_data() model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") @@ -200,7 +198,7 @@ def test_fit(): inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - inputs = MultiModalHuggingFaceInput(**inputs) + inputs = HuggingFaceMultiModalInput(**inputs) optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) art_classifier = HFMMPyTorch( model, @@ -230,7 +228,7 @@ def test_predict(): clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224), ) - inputs = MultiModalHuggingFaceInput(**inputs) + inputs = HuggingFaceMultiModalInput(**inputs) preds = art_classifier.predict(inputs) print("Pred shape is ", preds.shape) @@ -240,10 +238,9 @@ def test_adv_train(): import torch from transformers import CLIPProcessor, CLIPModel from art.defences.trainer import AdversarialTrainer - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - (x_train, y_train), (x_test, y_test) = get_cifar_data() + (x_train, y_train), (_, _) = get_cifar_data() model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, _, num_classes = get_and_process_input() From 9241fead8263ad19f6f1aeda362ff733f4ce5ad8 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 28 Nov 2023 15:19:18 +0000 Subject: [PATCH 22/46] re-add original test bash script Signed-off-by: GiulioZizzo --- run_tests.sh | 163 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 160 insertions(+), 3 deletions(-) diff --git a/run_tests.sh b/run_tests.sh index 64c81b0658..101c8d2705 100755 --- a/run_tests.sh +++ b/run_tests.sh @@ -19,9 +19,166 @@ then echo "############### Running tests with framework $framework ###############" echo "#######################################################################" - pytest --cov-report=xml --cov=art --cov-append -q -vv tests/attacks/evasion/test_multimodal_attack.py --framework=$framework --durations=0 - if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed multimodal tests"; fi + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/defences/detector/evasion --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/detector/evasion tests"; fi + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/defences/detector/poison/test_spectral_signature_defense.py --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/detector/poison/test_spectral_signature_defense.py tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/defences/preprocessor --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/preprocessor tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/defences/trainer --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/trainer tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/defences/transformer --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/transformer tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/preprocessing/audio --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed preprocessing/audio tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/preprocessing/image --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed preprocessing/image tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/preprocessing/expectation_over_transformation --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed preprocessing/expectation_over_transformation tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/utils --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed utils tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv -s tests/attacks/poison/ --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/poison tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv -s tests/attacks/evasion/ --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/evasion"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/speech_recognition/ --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/speech_recognition tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/attacks/inference/ --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/inference"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/classifiersFrameworks/ --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed classifiersFrameworks tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/classification/test_deeplearning_common.py --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/classification/test_deeplearning_common.py $framework"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/classification/test_deeplearning_specific.py --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/classification tests for framework $framework"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/certification/ --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/certification tests for framework $framework"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/classification/test_blackbox_existing_predictions.py --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/classification/test_blackbox_existing_predictions.py $framework"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/regression/test_blackbox.py --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/regression/test_blackbox.py $framework"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/metrics/privacy --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed metrics/privacy tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/evaluations --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed evaluations tests"; fi + + pytest --cov-report=xml --cov=art --cov-append -q -vv tests/test_summary_writer.py --framework=$framework --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed summary writer tests"; fi + +else + declare -a attacks_1=("tests/attacks/test_adversarial_patch.py" \ + "tests/attacks/test_adversarial_embedding.py" \ + "tests/attacks/test_backdoor_attack.py" \ + "tests/attacks/test_carlini.py" \ + "tests/attacks/test_copycat_cnn.py" \ + "tests/attacks/test_decision_tree_attack.py" \ + "tests/attacks/test_deepfool.py" \ + "tests/attacks/test_elastic_net.py" \ + "tests/attacks/test_feature_collision.py" \ + "tests/attacks/test_functionally_equivalent_extraction.py" \ + "tests/attacks/test_graphite.py" \ + "tests/attacks/test_hclu.py" \ + "tests/attacks/test_input_filter.py" \ + "tests/attacks/test_hop_skip_jump.py" \ + "tests/attacks/test_iterative_method.py" \ + "tests/attacks/test_knockoff_nets.py" ) + + declare -a attacks_2=("tests/attacks/test_newtonfool.py" \ + "tests/attacks/test_poisoning_attack_svm.py" \ + "tests/attacks/test_projected_gradient_descent.py" \ + "tests/attacks/test_saliency_map.py" \ + "tests/attacks/test_spatial_transformation.py" \ + "tests/attacks/test_universal_perturbation.py" \ + "tests/attacks/test_virtual_adversarial.py" \ + "tests/attacks/test_zoo.py" \ + "tests/attacks/test_pixel_attack.py" \ + "tests/attacks/test_threshold_attack.py" \ + "tests/attacks/test_wasserstein.py" \ + "tests/attacks/test_shapeshifter.py" \ + "tests/attacks/test_targeted_universal_perturbation.py" \ + "tests/attacks/test_simba.py" ) + + declare -a estimators=("tests/estimators/classification/test_blackbox.py" \ + "tests/estimators/classification/test_catboost.py" \ + "tests/estimators/classification/test_classifier.py" \ + "tests/estimators/classification/test_deep_partition_ensemble.py" \ + "tests/estimators/classification/test_detector_classifier.py" \ + "tests/estimators/classification/test_ensemble.py" \ + "tests/estimators/classification/test_GPy.py" \ + "tests/estimators/classification/test_input_filter.py" \ + "tests/estimators/classification/test_lightgbm.py" \ + "tests/estimators/classification/test_query_efficient_bb.py" \ + "tests/estimators/classification/test_scikitlearn.py" \ + "tests/estimators/classification/test_xgboost.py" \ + "tests/estimators/regression/test_scikitlearn.py" ) + + declare -a defences=("tests/defences/test_adversarial_trainer.py" \ + "tests/defences/test_class_labels.py" \ + "tests/defences/test_defensive_distillation.py" \ + "tests/defences/test_feature_squeezing.py" \ + "tests/defences/test_gaussian_augmentation.py" \ + "tests/defences/test_gaussian_noise.py" \ + "tests/defences/test_high_confidence.py" \ + "tests/defences/test_label_smoothing.py" \ + "tests/defences/test_neural_cleanse.py" \ + "tests/defences/test_pixel_defend.py" \ + "tests/defences/test_reverse_sigmoid.py" \ + "tests/defences/test_rounded.py" \ + "tests/defences/test_thermometer_encoding.py" \ + "tests/defences/test_variance_minimization.py" \ + "tests/defences/detector/poison/test_activation_defence.py" \ + "tests/defences/detector/poison/test_clustering_analyzer.py" \ + "tests/defences/detector/poison/test_ground_truth_evaluator.py" \ + "tests/defences/detector/poison/test_provenance_defence.py" \ + "tests/defences/detector/poison/test_roni.py" ) + + declare -a metrics=("tests/metrics/test_gradient_check.py" \ + "tests/metrics/test_metrics.py" \ + "tests/metrics/test_verification_decision_trees.py" ) + + declare -a art=("tests/test_data_generators.py" \ + "tests/test_optimizers.py" \ + "tests/test_utils.py" \ + "tests/test_visualization.py" ) + + # ----------------------------------------------------------------------------------------------- CODE TO RUN TESTS + + run_test () { + test=$1 + test_file_name="$(echo ${test} | rev | cut -d'/' -f1 | rev)" + + echo $'\n\n' + echo "######################################################################" + echo ${test} + echo "######################################################################" + pytest --cov-report=xml --cov=art --cov-append -q -vv ${test} --durations=0 + if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed $test"; fi + } + + tests="$legacy_module[@]" + for test in "${!tests}"; do + run_test ${test} + done fi -exit ${exit_code} +exit ${exit_code} \ No newline at end of file From 19f6493bde18f87fc9cb5dc13060d7e625bc3968 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 28 Nov 2023 16:01:40 +0000 Subject: [PATCH 23/46] updated naming Signed-off-by: GiulioZizzo --- .../huggingface_multimodal/__init__.py | 4 +-- .../{huggingface_mm.py => hugging_face_mm.py} | 4 +-- ...mm_inputs.py => hugging_face_mm_inputs.py} | 0 .../attacks/evasion/test_multimodal_attack.py | 28 +++++++++++++------ 4 files changed, 24 insertions(+), 12 deletions(-) rename art/experimental/estimators/huggingface_multimodal/{huggingface_mm.py => hugging_face_mm.py} (98%) rename art/experimental/estimators/huggingface_multimodal/{huggingface_mm_inputs.py => hugging_face_mm_inputs.py} (100%) diff --git a/art/experimental/estimators/huggingface_multimodal/__init__.py b/art/experimental/estimators/huggingface_multimodal/__init__.py index 22d1ff20c4..10f8835d0c 100644 --- a/art/experimental/estimators/huggingface_multimodal/__init__.py +++ b/art/experimental/estimators/huggingface_multimodal/__init__.py @@ -1,5 +1,5 @@ """ Module containing estimators for CLIP. """ -from art.experimental.estimators.huggingface_multimodal.huggingface_mm import HFMMPyTorch -from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput +from art.experimental.estimators.huggingface_multimodal.hugging_face_mm import HuggingFaceMulitModalPyTorch +from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py similarity index 98% rename from art/experimental/estimators/huggingface_multimodal/huggingface_mm.py rename to art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py index e8d64b93f9..871a53b35e 100644 --- a/art/experimental/estimators/huggingface_multimodal/huggingface_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py @@ -40,7 +40,7 @@ logger = logging.getLogger(__name__) -class HFMMPyTorch(PyTorchEstimator): +class HuggingFaceMulitModalPyTorch(PyTorchEstimator): """ This module implements an estimator for attacking pre-trained CLIP by adversarial perturbations on the image. Currently only supports PGD attacks. @@ -243,7 +243,7 @@ def predict( :param batch_size: Batch size. :return: Predictions over the supplied data. """ - from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput # Set model to evaluation mode self._model.eval() diff --git a/art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py similarity index 100% rename from art/experimental/estimators/huggingface_multimodal/huggingface_mm_inputs.py rename to art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 12f8672142..3763e5c6d0 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -45,7 +45,10 @@ def test_grad_equivalence(max_iter): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalInput, + ) def grad_art(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") @@ -53,7 +56,7 @@ def grad_art(): my_input = HuggingFaceMultiModalInput(**inputs) for _ in range(max_iter): - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, loss=torch.nn.CrossEntropyLoss(), input_shape=(3, 224, 224), @@ -96,7 +99,10 @@ def test_perturbation_equivalence(to_batch): from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalInput, + ) from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy def attack_clip(): @@ -107,7 +113,7 @@ def attack_clip(): original_image = inputs.pixel_values.clone().cpu().numpy() my_input = HuggingFaceMultiModalInput(**inputs) - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), @@ -168,7 +174,10 @@ def test_attack_functionality(): from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalInput, + ) from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy std = np.asarray([0.26862954, 0.26130258, 0.27577711]) @@ -199,7 +208,7 @@ def norm_bound_eps(eps_bound=None): original_images = np.stack(original_images) - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), @@ -244,12 +253,15 @@ def norm_bound_eps(eps_bound=None): def test_predict(): import torch from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalInput, + ) model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), From 09c84618522fda383ea04cba4b1eca21465147d4 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Tue, 28 Nov 2023 16:45:18 +0000 Subject: [PATCH 24/46] mypy fixes Signed-off-by: GiulioZizzo --- .../estimators/huggingface_multimodal/hugging_face_mm.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py index 871a53b35e..5f7fca8a40 100644 --- a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py @@ -225,10 +225,7 @@ def loss_gradient( # pylint: disable=W0613 grads = grads / self.clip_values[1] if not self.channels_first: - if isinstance(x, np.ndarray): - grads = np.transpose(grads, (0, 2, 3, 1)) - else: - grads = torch.permute(grads, (0, 2, 3, 1)) + grads = torch.permute(grads, (0, 2, 3, 1)) assert grads.shape == x["pixel_values"].shape return grads.cpu().numpy() @@ -243,7 +240,7 @@ def predict( :param batch_size: Batch size. :return: Predictions over the supplied data. """ - from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput # Set model to evaluation mode self._model.eval() From a372550a5293959e225176272108eafbc600fa84 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Wed, 29 Nov 2023 15:39:04 +0000 Subject: [PATCH 25/46] updating tests Signed-off-by: GiulioZizzo --- .../hugging_face_mm_inputs.py | 3 + clip_dev.py | 98 ++++++++++++++++-- .../attacks/evasion/test_multimodal_attack.py | 39 ++++--- utils/data/images/birds.npy | Bin 0 -> 196736 bytes utils/data/images/ferns.npy | Bin 0 -> 196736 bytes utils/data/images/flowers.npy | Bin 0 -> 196736 bytes utils/data/images/forest.npy | Bin 0 -> 196736 bytes 7 files changed, 113 insertions(+), 27 deletions(-) create mode 100644 utils/data/images/birds.npy create mode 100644 utils/data/images/ferns.npy create mode 100644 utils/data/images/flowers.npy create mode 100644 utils/data/images/forest.npy diff --git a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py index 75f4047a8b..ecec01d7f8 100644 --- a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py @@ -78,10 +78,13 @@ def __setitem__(self, key, value): pixel_values[key] = torch.tensor(value) super().__setitem__("pixel_values", pixel_values) assert self["pixel_values"].shape == original_shape + elif isinstance(key, np.ndarray): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[key] super().__setitem__("pixel_values", pixel_values) + self.shape = pixel_values.shape + self.ndim = pixel_values.ndim else: raise ValueError( f"Unsupported key {key} with type {type(key)}, " diff --git a/clip_dev.py b/clip_dev.py index 01fb53056a..4c59545388 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,6 +1,6 @@ import numpy as np -from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput -from art.attacks.evasion import ProjectedGradientDescent +from art.experimental.estimators.huggingface_multimodal import HuggingFaceMulitModalPyTorch, HuggingFaceMultiModalInput +from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy import torch from torchvision import datasets @@ -76,6 +76,64 @@ def get_cifar_data(): return (x_train[0:250], y_train[0:250]), (x_test[0:250], y_test[0:250]) +def attack_clip_plant_pgd(): + from PIL import Image + + from transformers import CLIPProcessor, CLIPModel + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + loss_fn = torch.nn.CrossEntropyLoss() + + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of some plants", "a photo of a dog", "a photo of a car"] + + # url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open("ART_Test_Image.jpg") + image = np.array(image) + np.save("ART_Test_Image.npy", image) + # make a batch + input_list = [] + input_text = [] + for _ in range(1): + input_list.append(image) + input_text.append(text) + + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() + + art_classifier = HuggingFaceMulitModalPyTorch( + model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) + ) + + my_input = HuggingFaceMultiModalInput(**inputs) + + labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) + # loss = art_classifier._get_losses(my_input, labels) + # grad = art_classifier.loss_gradient(my_input, labels) + clean_preds = art_classifier.predict(my_input) + print(clean_preds) + + print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) + + attack = CLIPProjectedGradientDescentNumpy( + art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) + x_adv = attack.generate(my_input, labels) + adv_preds = art_classifier.predict(x_adv) + + eps = norm_bound_eps() + + np.save("eps_mins.npy", original_image - eps.reshape((1, 3, 1, 1))) + np.save("eps_maxs.npy", original_image + eps.reshape((1, 3, 1, 1))) + np.save("original_image.npy", original_image) + + print(adv_preds) + + def attack_clip_pgd(): from PIL import Image import requests @@ -101,7 +159,7 @@ def attack_clip_pgd(): original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) @@ -113,7 +171,7 @@ def attack_clip_pgd(): clean_preds = art_classifier.predict(my_input) print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) - attack = ProjectedGradientDescent( + attack = CLIPProjectedGradientDescentNumpy( art_classifier, max_iter=10, eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), @@ -139,6 +197,7 @@ def cifar_clip_pgd(): from transformers import CLIPProcessor, CLIPModel from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy + """ text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] labels = torch.tensor(np.asarray([0, 1, 3, 4])) @@ -147,6 +206,24 @@ def cifar_clip_pgd(): for fname in ["000000039769.jpg", "000000000285.jpg", "000000002006.jpg", "000000002149.jpg"]: url = "http://images.cocodataset.org/val2017/" + fname input_list.append(Image.open(requests.get(url, stream=True).raw)) + """ + text = [ + "a photo of pink flowers", + "a photo of birds by the sea", + "a photo of a forest", + "a photo of a fern", + "a photo of a bus", + ] + + input_list = [] + for fname in ["flowers", "birds", "forest", "ferns"]: + image = Image.open(fname + ".jpg") + image = np.array(image) + np.save(fname + ".npy", image) + print("image shape is ", image.shape) + input_list.append(image) + + labels = torch.tensor(np.asarray([0, 1, 2, 3])) model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") @@ -157,14 +234,14 @@ def cifar_clip_pgd(): for i in range(3): original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) - original_images = np.concatenate(original_images) + original_images = np.stack(original_images) + print("input shape is ", original_images.shape) - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), input_shape=(3, 224, 224), - nb_classes=5, ) my_input = HuggingFaceMultiModalInput(**inputs) @@ -200,7 +277,7 @@ def test_fit(): inputs = HuggingFaceMultiModalInput(**inputs) optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, optimizer=optimizer, nb_classes=10, @@ -221,7 +298,7 @@ def test_predict(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels, num_classes = get_and_process_input() - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model, nb_classes=num_classes, loss=torch.nn.CrossEntropyLoss(), @@ -252,7 +329,7 @@ def test_adv_train(): inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - art_classifier = HFMMPyTorch( + art_classifier = HuggingFaceMulitModalPyTorch( model.to(device), nb_classes=num_classes, optimizer=optimizer, @@ -283,3 +360,4 @@ def test_adv_train(): # test_fit() # attack_clip_pgd() cifar_clip_pgd() +# attack_clip_plant_pgd() diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 3763e5c6d0..bd4178ae60 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -1,19 +1,19 @@ +import os import numpy as np import pytest def get_and_process_input(to_one_hot=False, return_batch=False): - from PIL import Image - import requests import torch from transformers import CLIPProcessor processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of a cat", "a photo of a dog", "a photo of a bear"] + text = ["a photo of pink flowers", "a photo of a dog", "a photo of a bear"] - url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open(requests.get(url, stream=True).raw) + fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/flowers.npy") + + image = np.load(fpath) if return_batch: input_list = [] @@ -75,7 +75,7 @@ def manual_grad(): lossfn = torch.nn.CrossEntropyLoss() for _ in range(max_iter): outputs = model(**inputs) - logits_per_image = outputs.logits_per_image # image-text similarity score + logits_per_image = outputs.logits_per_image loss = lossfn(logits_per_image, labels.to(device)) loss.backward() @@ -123,7 +123,7 @@ def attack_clip(): attack = CLIPProjectedGradientDescentNumpy( art_classifier, max_iter=2, - eps=np.ones((3, 224, 224)) * 0.3, # np.reshape(norm_bound_eps(), (3, 1, 1)), + eps=np.ones((3, 224, 224)) * 0.3, eps_step=np.ones((3, 224, 224)) * 0.1, ) @@ -169,8 +169,6 @@ def manual_attack(): def test_attack_functionality(): import torch - import requests - from PIL import Image from transformers import CLIPProcessor, CLIPModel @@ -188,14 +186,22 @@ def norm_bound_eps(eps_bound=None): eps_bound = np.abs(eps_bound / std) return eps_bound - text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] + fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/") - labels = torch.tensor(np.asarray([0, 1, 3, 4])) + text = [ + "a photo of pink flowers", + "a photo of birds by the sea", + "a photo of a forest", + "a photo of a fern", + "a photo of a bus", + ] input_list = [] - for fname in ["000000039769.jpg", "000000000285.jpg", "000000002006.jpg", "000000002149.jpg"]: - url = "http://images.cocodataset.org/val2017/" + fname - input_list.append(Image.open(requests.get(url, stream=True).raw)) + for fname in ["flowers", "birds", "forest", "ferns"]: + image = np.load(os.path.join(fpath, fname + ".npy")) + input_list.append(image) + + labels = torch.tensor(np.asarray([0, 1, 2, 3])) model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") @@ -203,7 +209,7 @@ def norm_bound_eps(eps_bound=None): loss_fn = torch.nn.CrossEntropyLoss() inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) original_images = [] - for i in range(4): + for i in range(len(labels)): original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) original_images = np.stack(original_images) @@ -229,8 +235,7 @@ def norm_bound_eps(eps_bound=None): adv_preds = art_classifier.predict(x_adv) adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - x_adv = x_adv["pixel_values"] - x_adv = x_adv.cpu().detach().numpy() + x_adv = x_adv["pixel_values"].cpu().detach().numpy() assert np.all(x_adv >= np.min(original_images)) assert np.all(x_adv <= np.max(original_images)) diff --git a/utils/data/images/birds.npy b/utils/data/images/birds.npy new file mode 100644 index 0000000000000000000000000000000000000000..e3c5b5243b615068f4a1c1d6d05a2dd6d9203b25 GIT binary patch literal 196736 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""" -from art.experimental.estimators.huggingface_multimodal.hugging_face_mm import HuggingFaceMulitModalPyTorch +from art.experimental.estimators.huggingface_multimodal.hugging_face_mm import HuggingFaceMultiModalPyTorch from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput diff --git a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py index 5f7fca8a40..4617906eef 100644 --- a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py @@ -40,7 +40,7 @@ logger = logging.getLogger(__name__) -class HuggingFaceMulitModalPyTorch(PyTorchEstimator): +class HuggingFaceMultiModalPyTorch(PyTorchEstimator): """ This module implements an estimator for attacking pre-trained CLIP by adversarial perturbations on the image. Currently only supports PGD attacks. diff --git a/clip_dev.py b/clip_dev.py index 4c59545388..387f964298 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -1,5 +1,5 @@ import numpy as np -from art.experimental.estimators.huggingface_multimodal import HuggingFaceMulitModalPyTorch, HuggingFaceMultiModalInput +from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy import torch @@ -102,7 +102,7 @@ def attack_clip_plant_pgd(): original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) @@ -192,7 +192,6 @@ def attack_clip_pgd(): def cifar_clip_pgd(): from PIL import Image - import requests from transformers import CLIPProcessor, CLIPModel from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy @@ -237,7 +236,7 @@ def cifar_clip_pgd(): original_images = np.stack(original_images) print("input shape is ", original_images.shape) - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), @@ -277,7 +276,7 @@ def test_fit(): inputs = HuggingFaceMultiModalInput(**inputs) optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, optimizer=optimizer, nb_classes=10, @@ -298,7 +297,7 @@ def test_predict(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels, num_classes = get_and_process_input() - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, nb_classes=num_classes, loss=torch.nn.CrossEntropyLoss(), @@ -329,7 +328,7 @@ def test_adv_train(): inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model.to(device), nb_classes=num_classes, optimizer=optimizer, diff --git a/notebooks/clip_attack.ipynb b/notebooks/clip_attack.ipynb index 161967fb8b..4e50653e81 100644 --- a/notebooks/clip_attack.ipynb +++ b/notebooks/clip_attack.ipynb @@ -19,11 +19,10 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", "import numpy as np\n", "import torch\n", - "sys.path.append('../')\n", - "from art.experimental.estimators.huggingface_multimodal import HFMMPyTorch, HuggingFaceMultiModalInput\n", + "\n", + "from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput\n", "from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy\n", "\n", "# Image normalization numbers\n", @@ -115,10 +114,9 @@ " original_images.append(inputs[\"pixel_values\"][i].clone().cpu().detach().numpy())\n", " original_images = np.concatenate(original_images)\n", "\n", - " art_classifier = HFMMPyTorch(\n", + " art_classifier = HuggingFaceMultiModalPyTorch(\n", " model, \n", " loss=loss_fn,\n", - " nb_classes=5,\n", " clip_values=(np.min(original_images), np.max(original_images)), \n", " input_shape=(3, 224, 224)\n", " )\n", @@ -144,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "5f011a60-2381-4d3f-866a-a39ae2279dde", "metadata": {}, "outputs": [ @@ -152,7 +150,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 38.93it/s]" + "2023-11-30 09:48:11.240678: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.70it/s]" ] }, { @@ -172,7 +188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eefe09b660a046c6a2fb3a24ca5104f3", + "model_id": "d733d074704040ec8eb12855556f4922", "version_major": 2, "version_minor": 0 }, @@ -187,7 +203,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 38.42it/s]" + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.95it/s]" ] }, { @@ -213,7 +229,7 @@ { "cell_type": "code", "execution_count": null, - "id": "88bb203f-d665-44f7-b5a0-40fa28e9deb2", + "id": "031a35b7", "metadata": {}, "outputs": [], "source": [] @@ -235,7 +251,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index bd4178ae60..6fbe799fbf 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -46,7 +46,7 @@ def test_grad_equivalence(max_iter): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from transformers import CLIPModel from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -56,7 +56,7 @@ def grad_art(): my_input = HuggingFaceMultiModalInput(**inputs) for _ in range(max_iter): - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=torch.nn.CrossEntropyLoss(), input_shape=(3, 224, 224), @@ -100,7 +100,7 @@ def test_perturbation_equivalence(to_batch): from transformers import CLIPModel from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy @@ -113,7 +113,7 @@ def attack_clip(): original_image = inputs.pixel_values.clone().cpu().numpy() my_input = HuggingFaceMultiModalInput(**inputs) - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), @@ -173,7 +173,7 @@ def test_attack_functionality(): from transformers import CLIPProcessor, CLIPModel from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy @@ -214,7 +214,7 @@ def norm_bound_eps(eps_bound=None): original_images = np.stack(original_images) - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_images), np.max(original_images)), @@ -259,14 +259,14 @@ def test_predict(): import torch from transformers import CLIPModel from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMulitModalPyTorch, + HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), From 5afe1e3e1aa6b9cb7d9ec506748fa81d1d76e3ac Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 30 Nov 2023 11:08:01 +0000 Subject: [PATCH 27/46] moving some tests to new script for estimator Signed-off-by: GiulioZizzo --- .../huggingface_multimodal/hugging_face_mm.py | 24 ++-- clip_dev.py | 7 +- .../attacks/evasion/test_multimodal_attack.py | 23 +--- .../classification/test_multimodal.py | 110 ++++++++++++++++++ 4 files changed, 129 insertions(+), 35 deletions(-) create mode 100644 tests/estimators/classification/test_multimodal.py diff --git a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py index 4617906eef..8c6d295fcf 100644 --- a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py +++ b/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py @@ -16,7 +16,8 @@ # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """ -This module implements ... +This module implements an estimator for attacking pre-trained CLIP by adversarial perturbations on the image. +| Paper link: https://arxiv.org/abs/2103.00020 """ import logging import random @@ -202,6 +203,8 @@ def loss_gradient( # pylint: disable=W0613 :param x: Dictionary inputs for the CLIP model. :param y: Labels for the loss + :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported + and providing it takes no effect. :return: Loss gradients of the same shape as `x`. """ import torch @@ -238,6 +241,8 @@ def predict( :param x: Dictionary inputs for the CLIP model. :param batch_size: Batch size. + :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported + and providing it takes no effect. :return: Predictions over the supplied data. """ from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput @@ -268,28 +273,27 @@ def fit( # pylint: disable=W0221 batch_size: int = 128, nb_epochs: int = 10, scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"] = None, - verbose: bool = True, + display_progress_bar: bool = True, **kwargs, ) -> None: """ Fit the classifier on the training set `(x, y)`. :param x: Training data. - :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of - shape (nb_samples,). + :param y: Target values (class labels) in index labels style of shape (nb_samples,). :param batch_size: Size of batches. :param nb_epochs: Number of epochs to use for training. :param scheduler: Learning rate scheduler to run at the start of every epoch. - :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch + :param display_progress_bar: Displays the training progress. + :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported and providing it takes no effect. """ import torch - from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput + from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput self._model.train() if self._optimizer is None: raise ValueError("Please supply a optimizer") - # y_preprocessed = self.reduce_labels(y) y_tensor = torch.from_numpy(y) @@ -302,7 +306,7 @@ def fit( # pylint: disable=W0221 random.shuffle(ind) # Train for one epoch - pbar = tqdm(range(num_batch), disable=not verbose) + pbar = tqdm(range(num_batch), disable=not display_progress_bar) accs = [] losses = [] @@ -345,7 +349,7 @@ def fit( # pylint: disable=W0221 ) / len(y_batch) accs.append(acc) - if verbose: + if display_progress_bar: pbar.set_description(f"Loss {np.mean(np.stack(losses)):.2f} " f"Acc {np.mean(np.stack(accs)):.2f} ") self.training_loss.append(losses) @@ -367,6 +371,8 @@ def compute_loss( # type: ignore :param x: Dictionary inputs for the CLIP model. :param y: Target values + :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported + and providing it takes no effect. :return: Loss. """ import torch diff --git a/clip_dev.py b/clip_dev.py index 387f964298..d84510ccf4 100644 --- a/clip_dev.py +++ b/clip_dev.py @@ -159,7 +159,7 @@ def attack_clip_pgd(): original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - art_classifier = HuggingFaceMulitModalPyTorch( + art_classifier = HuggingFaceMultiModalPyTorch( model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) ) @@ -279,7 +279,6 @@ def test_fit(): art_classifier = HuggingFaceMultiModalPyTorch( model, optimizer=optimizer, - nb_classes=10, loss=torch.nn.CrossEntropyLoss(), clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224), @@ -356,7 +355,7 @@ def test_adv_train(): # test_adv_train() # test_predict() -# test_fit() +test_fit() # attack_clip_pgd() -cifar_clip_pgd() +# cifar_clip_pgd() # attack_clip_plant_pgd() diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 6fbe799fbf..0338603593 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -237,6 +237,7 @@ def norm_bound_eps(eps_bound=None): x_adv = x_adv["pixel_values"].cpu().detach().numpy() + # Assert valid adversarial examples assert np.all(x_adv >= np.min(original_images)) assert np.all(x_adv <= np.max(original_images)) @@ -252,25 +253,3 @@ def norm_bound_eps(eps_bound=None): assert clean_acc == 1.0 assert adv_acc == 0.0 - - -@pytest.mark.only_with_platform("huggingface") -def test_predict(): - import torch - from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMultiModalPyTorch, - HuggingFaceMultiModalInput, - ) - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) - - art_classifier = HuggingFaceMultiModalPyTorch( - model, - loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - inputs = HuggingFaceMultiModalInput(**inputs) - _ = art_classifier.predict(inputs) diff --git a/tests/estimators/classification/test_multimodal.py b/tests/estimators/classification/test_multimodal.py new file mode 100644 index 0000000000..762e671217 --- /dev/null +++ b/tests/estimators/classification/test_multimodal.py @@ -0,0 +1,110 @@ +import os +import numpy as np +import pytest + +from art.utils import load_dataset + + +@pytest.fixture() +def fix_get_cifar10_data(): + """ + Get the first 128 samples of the cifar10 test set + + :return: First 128 sample/label pairs of the cifar10 test dataset. + """ + nb_test = 128 + + (_, _), (x_test, y_test), _, _ = load_dataset("cifar10") + y_test = np.argmax(y_test, axis=1) + x_test, y_test = x_test[:nb_test], y_test[:nb_test] + x_test = np.transpose(x_test, (0, 3, 1, 2)) # return in channels first format + return x_test.astype(np.float32), y_test + + +def get_and_process_input(to_one_hot=False, return_batch=False): + + import torch + from transformers import CLIPProcessor + + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + text = ["a photo of pink flowers", "a photo of a dog", "a photo of a bear"] + + fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/flowers.npy") + + image = np.load(fpath) + + if return_batch: + input_list = [] + for _ in range(10): + input_list.append(image) + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().numpy() + if to_one_hot: + labels = np.zeros((10, 3)) + labels = labels[0:10] + 1 + else: + labels = np.zeros((10,)) + + labels = torch.tensor(labels).type(torch.LongTensor) + + else: + + inputs = processor(text=text, images=image, return_tensors="pt", padding=True) + original_image = inputs.pixel_values.clone().cpu().numpy() + labels = torch.tensor(np.asarray([0])) + + return inputs, original_image, labels, len(text) + + +@pytest.mark.only_with_platform("huggingface") +def test_predict(): + import torch + from transformers import CLIPModel + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) + + art_classifier = HuggingFaceMultiModalPyTorch( + model, + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), + ) + inputs = HuggingFaceMultiModalInput(**inputs) + _ = art_classifier.predict(inputs) + + +@pytest.mark.only_with_platform("huggingface") +def test_fit(fix_get_cifar10_data): + import torch + from transformers import CLIPProcessor, CLIPModel + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) + + x_train = fix_get_cifar10_data[0] + y_train = fix_get_cifar10_data[1] + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + + text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] + inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() + + inputs = HuggingFaceMultiModalInput(**inputs) + optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + art_classifier = HuggingFaceMultiModalPyTorch( + model, + optimizer=optimizer, + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), + ) + + art_classifier.fit(inputs, y_train, nb_epochs=1) From d85b2673d3a9ec75912715dfda61a274d72dcb97 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 30 Nov 2023 13:46:52 +0000 Subject: [PATCH 28/46] remove development files Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 1 - .github/workflows/ci-style-checks.yml | 2 - clip_dev.py | 361 -------------------------- 3 files changed, 364 deletions(-) delete mode 100644 clip_dev.py diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index 5cbd57ba70..ed3056ad06 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -16,7 +16,6 @@ on: branches: - main - dev* - - clip_1.17_dev # Run scheduled CI flow daily schedule: diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml index 499a18cb5d..c8283c8b9d 100644 --- a/.github/workflows/ci-style-checks.yml +++ b/.github/workflows/ci-style-checks.yml @@ -16,8 +16,6 @@ on: branches: - main - dev* - - clip_1.17_dev - # Run scheduled CI flow daily schedule: diff --git a/clip_dev.py b/clip_dev.py deleted file mode 100644 index d84510ccf4..0000000000 --- a/clip_dev.py +++ /dev/null @@ -1,361 +0,0 @@ -import numpy as np -from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput -from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - -import torch -from torchvision import datasets - -import ssl - -ssl._create_default_https_context = ssl._create_unverified_context - -MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073]) -STD = np.asarray([0.26862954, 0.26130258, 0.27577711]) -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -def get_and_process_input(to_one_hot=False, return_batch=False): - - from PIL import Image - import requests - import torch - from transformers import CLIPProcessor - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of a cat", "a photo of a dog", "a photo of a bear"] - - url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open(requests.get(url, stream=True).raw) - - if return_batch: - input_list = [] - for _ in range(10): - input_list.append(image) - inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) - original_image = inputs["pixel_values"][0].clone().cpu().numpy() - if to_one_hot: - labels = np.zeros((10, 3)) - labels = labels[0:10] + 1 - else: - labels = np.zeros((10,)) - - labels = torch.tensor(labels).type(torch.LongTensor) - - else: - - inputs = processor(text=text, images=image, return_tensors="pt", padding=True) - original_image = inputs.pixel_values.clone().cpu().numpy() - labels = torch.tensor(np.asarray([0])) - - return inputs, original_image, labels, len(text) - - -def norm_bound_eps(eps_bound=None): - if eps_bound is None: - eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) - eps_bound = np.abs(eps_bound / STD) - return eps_bound - - -def get_cifar_data(): - train_set = datasets.CIFAR10("./data", train=True, download=True) - test_set = datasets.CIFAR10("./data", train=False, download=True) - - x_train = train_set.data.astype(np.float32) - y_train = np.asarray(train_set.targets) - - x_test = test_set.data.astype(np.float32) - y_test = np.asarray(test_set.targets) - - x_train = np.moveaxis(x_train, [3], [1]) - x_test = np.moveaxis(x_test, [3], [1]) - - x_train = x_train / 255.0 - x_test = x_test / 255.0 - - return (x_train[0:250], y_train[0:250]), (x_test[0:250], y_test[0:250]) - - -def attack_clip_plant_pgd(): - from PIL import Image - - from transformers import CLIPProcessor, CLIPModel - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - loss_fn = torch.nn.CrossEntropyLoss() - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of some plants", "a photo of a dog", "a photo of a car"] - - # url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open("ART_Test_Image.jpg") - image = np.array(image) - np.save("ART_Test_Image.npy", image) - # make a batch - input_list = [] - input_text = [] - for _ in range(1): - input_list.append(image) - input_text.append(text) - - inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) - - original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - - art_classifier = HuggingFaceMultiModalPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) - ) - - my_input = HuggingFaceMultiModalInput(**inputs) - - labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) - # loss = art_classifier._get_losses(my_input, labels) - # grad = art_classifier.loss_gradient(my_input, labels) - clean_preds = art_classifier.predict(my_input) - print(clean_preds) - - print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) - - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=10, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1, - ) - x_adv = attack.generate(my_input, labels) - adv_preds = art_classifier.predict(x_adv) - - eps = norm_bound_eps() - - np.save("eps_mins.npy", original_image - eps.reshape((1, 3, 1, 1))) - np.save("eps_maxs.npy", original_image + eps.reshape((1, 3, 1, 1))) - np.save("original_image.npy", original_image) - - print(adv_preds) - - -def attack_clip_pgd(): - from PIL import Image - import requests - - from transformers import CLIPProcessor, CLIPModel - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - loss_fn = torch.nn.CrossEntropyLoss() - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of a cat", "a photo of a dog", "a photo of a car"] - - url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open(requests.get(url, stream=True).raw) - # make a batch - input_list = [] - input_text = [] - for _ in range(10): - input_list.append(image) - input_text.append(text) - - inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) - - original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - - art_classifier = HuggingFaceMultiModalPyTorch( - model, loss=loss_fn, clip_values=(np.min(original_image), np.max(original_image)), input_shape=(3, 224, 224) - ) - - my_input = HuggingFaceMultiModalInput(**inputs) - - labels = torch.tensor(np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) - # loss = art_classifier._get_losses(my_input, labels) - # grad = art_classifier.loss_gradient(my_input, labels) - clean_preds = art_classifier.predict(my_input) - print("The max perturbation is", np.max(np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)))) - - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=10, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1, - ) - x_adv = attack.generate(my_input, labels) - adv_preds = art_classifier.predict(x_adv) - - eps = norm_bound_eps() - - np.save("eps_mins.npy", original_image - eps.reshape((1, 3, 1, 1))) - np.save("eps_maxs.npy", original_image + eps.reshape((1, 3, 1, 1))) - np.save("original_image.npy", original_image) - - print(clean_preds) - print(adv_preds) - - -def cifar_clip_pgd(): - from PIL import Image - - from transformers import CLIPProcessor, CLIPModel - from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - - """ - text = ["a photo of a cat", "a photo of a bear", "a photo of a car", "a photo of a bus", "apples"] - - labels = torch.tensor(np.asarray([0, 1, 3, 4])) - - input_list = [] - for fname in ["000000039769.jpg", "000000000285.jpg", "000000002006.jpg", "000000002149.jpg"]: - url = "http://images.cocodataset.org/val2017/" + fname - input_list.append(Image.open(requests.get(url, stream=True).raw)) - """ - text = [ - "a photo of pink flowers", - "a photo of birds by the sea", - "a photo of a forest", - "a photo of a fern", - "a photo of a bus", - ] - - input_list = [] - for fname in ["flowers", "birds", "forest", "ferns"]: - image = Image.open(fname + ".jpg") - image = np.array(image) - np.save(fname + ".npy", image) - print("image shape is ", image.shape) - input_list.append(image) - - labels = torch.tensor(np.asarray([0, 1, 2, 3])) - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - - loss_fn = torch.nn.CrossEntropyLoss() - inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) - original_images = [] - for i in range(3): - original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) - - original_images = np.stack(original_images) - print("input shape is ", original_images.shape) - - art_classifier = HuggingFaceMultiModalPyTorch( - model, - loss=loss_fn, - clip_values=(np.min(original_images), np.max(original_images)), - input_shape=(3, 224, 224), - ) - - my_input = HuggingFaceMultiModalInput(**inputs) - clean_preds = art_classifier.predict(my_input) - print(clean_preds) - clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - print("clean acc ", clean_acc) - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=10, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1, - ) - x_adv = attack.generate(my_input, labels) - adv_preds = art_classifier.predict(x_adv) - adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - print("adv_acc ", adv_acc) - - print(clean_preds) - print(adv_preds) - - -def test_fit(): - from transformers import CLIPProcessor, CLIPModel - - (x_train, y_train), (_, _) = get_cifar_data() - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - - text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] - inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) - original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - - inputs = HuggingFaceMultiModalInput(**inputs) - optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) - art_classifier = HuggingFaceMultiModalPyTorch( - model, - optimizer=optimizer, - loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - - num_of_samples = len(inputs) - print(num_of_samples) - art_classifier.fit(inputs, y_train) - - -def test_predict(): - import torch - from transformers import CLIPModel - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels, num_classes = get_and_process_input() - - art_classifier = HuggingFaceMultiModalPyTorch( - model, - nb_classes=num_classes, - loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - inputs = HuggingFaceMultiModalInput(**inputs) - - preds = art_classifier.predict(inputs) - print("Pred shape is ", preds.shape) - - -def test_adv_train(): - import torch - from transformers import CLIPProcessor, CLIPModel - from art.defences.trainer import AdversarialTrainer - from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - - (x_train, y_train), (_, _) = get_cifar_data() - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, _, num_classes = get_and_process_input() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - - text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] - inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) - original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - - art_classifier = HuggingFaceMultiModalPyTorch( - model.to(device), - nb_classes=num_classes, - optimizer=optimizer, - loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=10, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1, - ) - - trainer = AdversarialTrainer( - art_classifier, - attacks=attack, - ratio=1.0, - ) - inputs = HuggingFaceMultiModalInput(**inputs) - - trainer.fit(inputs, y_train) - - -# test_adv_train() -# test_predict() -test_fit() -# attack_clip_pgd() -# cifar_clip_pgd() -# attack_clip_plant_pgd() From a6ccea1951e4ff9d0f6e68736eacd49761b08056 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 30 Nov 2023 14:36:24 +0000 Subject: [PATCH 29/46] updates to tests Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 2 +- .github/workflows/ci-style-checks.yml | 1 + .../attacks/evasion/test_multimodal_attack.py | 371 +++++++++--------- .../classification/test_multimodal.py | 158 ++++---- 4 files changed, 274 insertions(+), 258 deletions(-) diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index ed3056ad06..a223b163e0 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -16,7 +16,7 @@ on: branches: - main - dev* - + - clip_1.17_dev # Run scheduled CI flow daily schedule: - cron: '0 8 * * 0' diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml index c8283c8b9d..549a4de552 100644 --- a/.github/workflows/ci-style-checks.yml +++ b/.github/workflows/ci-style-checks.yml @@ -16,6 +16,7 @@ on: branches: - main - dev* + - clip_1.17_dev # Run scheduled CI flow daily schedule: diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index 0338603593..bff239fcaf 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -2,9 +2,13 @@ import numpy as np import pytest +from tests.utils import ARTTestException -def get_and_process_input(to_one_hot=False, return_batch=False): +def get_and_process_input(return_batch=False): + """ + Helper function to load relevant data. + """ import torch from transformers import CLIPProcessor @@ -21,16 +25,9 @@ def get_and_process_input(to_one_hot=False, return_batch=False): input_list.append(image) inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) original_image = inputs["pixel_values"][0].clone().cpu().numpy() - if to_one_hot: - labels = np.zeros((10, 3)) - labels = labels[0:10] + 1 - else: - labels = np.zeros((10,)) - + labels = np.zeros((10,)) labels = torch.tensor(labels).type(torch.LongTensor) - else: - inputs = processor(text=text, images=image, return_tensors="pt", padding=True) original_image = inputs.pixel_values.clone().cpu().numpy() labels = torch.tensor(np.asarray([0])) @@ -40,216 +37,232 @@ def get_and_process_input(to_one_hot=False, return_batch=False): @pytest.mark.only_with_platform("huggingface") @pytest.mark.parametrize("max_iter", [1, 5]) -def test_grad_equivalence(max_iter): - import torch +def test_grad_equivalence(max_iter, art_warning): + """ + Test that the gradient from using ART tools matches that obtained by manual calculation. + """ + try: + import torch + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + from transformers import CLIPModel + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMultiModalPyTorch, - HuggingFaceMultiModalInput, - ) + def grad_art(): + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) + + my_input = HuggingFaceMultiModalInput(**inputs) + for _ in range(max_iter): + art_classifier = HuggingFaceMultiModalPyTorch( + model, + loss=torch.nn.CrossEntropyLoss(), + input_shape=(3, 224, 224), + device_type="gpu", + ) + loss_grad = art_classifier.loss_gradient(my_input, labels) + return loss_grad + + def manual_grad(): + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + model = model.to(device) + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) + inputs = inputs.to(device) + + inputs.pixel_values.requires_grad_(True) + lossfn = torch.nn.CrossEntropyLoss() + for _ in range(max_iter): + outputs = model(**inputs) + logits_per_image = outputs.logits_per_image + + loss = lossfn(logits_per_image, labels.to(device)) + loss.backward() + + return inputs.pixel_values.grad + + art = grad_art() + manual = manual_grad() + assert np.allclose(art, manual.cpu().detach().numpy()) + except ARTTestException as e: + art_warning(e) - def grad_art(): - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) - my_input = HuggingFaceMultiModalInput(**inputs) - for _ in range(max_iter): - art_classifier = HuggingFaceMultiModalPyTorch( - model, - loss=torch.nn.CrossEntropyLoss(), - input_shape=(3, 224, 224), - device_type="gpu", - ) - loss_grad = art_classifier.loss_gradient(my_input, labels) - return loss_grad +@pytest.mark.only_with_platform("huggingface") +@pytest.mark.parametrize("to_batch", [False, True]) +def test_perturbation_equivalence(to_batch, art_warning): + """ + Test that the perturbation from using ART tools matches that obtained by manual calculation. + """ + try: + import torch - def manual_grad(): - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - model = model.to(device) - inputs, original_image, labels, num_classes = get_and_process_input(return_batch=False) - inputs = inputs.to(device) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - inputs.pixel_values.requires_grad_(True) - lossfn = torch.nn.CrossEntropyLoss() - for _ in range(max_iter): - outputs = model(**inputs) - logits_per_image = outputs.logits_per_image + from transformers import CLIPModel - loss = lossfn(logits_per_image, labels.to(device)) - loss.backward() + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) + from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - return inputs.pixel_values.grad + def attack_clip(): - art = grad_art() - manual = manual_grad() - assert np.allclose(art, manual.cpu().detach().numpy()) + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + loss_fn = torch.nn.CrossEntropyLoss() + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) + original_image = inputs.pixel_values.clone().cpu().numpy() + my_input = HuggingFaceMultiModalInput(**inputs) + art_classifier = HuggingFaceMultiModalPyTorch( + model, + loss=loss_fn, + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), + ) -@pytest.mark.only_with_platform("huggingface") -@pytest.mark.parametrize("to_batch", [False, True]) -def test_perturbation_equivalence(to_batch): - """ - Test that the perturbation from using ART tools matches that obtained by manual calculation. - """ - import torch + attack = CLIPProjectedGradientDescentNumpy( + art_classifier, + max_iter=2, + eps=np.ones((3, 224, 224)) * 0.3, + eps_step=np.ones((3, 224, 224)) * 0.1, + ) - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + perturbation = attack._compute_perturbation(my_input, labels, mask=None) - from transformers import CLIPModel + adv_art_x = attack._apply_perturbation(my_input[0:], perturbation, attack.eps_step) - from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMultiModalPyTorch, - HuggingFaceMultiModalInput, - ) - from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy + return perturbation, adv_art_x["pixel_values"].cpu().detach().numpy() - def attack_clip(): + def manual_attack(): - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - loss_fn = torch.nn.CrossEntropyLoss() - inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) - original_image = inputs.pixel_values.clone().cpu().numpy() + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + model = model.to(device) + inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) + lossfn = torch.nn.CrossEntropyLoss() + inputs = inputs.to(device) - my_input = HuggingFaceMultiModalInput(**inputs) - art_classifier = HuggingFaceMultiModalPyTorch( - model, - loss=loss_fn, - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) + inputs["pixel_values"] = inputs["pixel_values"].requires_grad_(True) - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=2, - eps=np.ones((3, 224, 224)) * 0.3, - eps_step=np.ones((3, 224, 224)) * 0.1, - ) + outputs = model(**inputs) + loss = lossfn(outputs.logits_per_image, labels.to(device)) + loss.backward() + sign = torch.sign(inputs["pixel_values"].grad) - perturbation = attack._compute_perturbation(my_input, labels, mask=None) + init_max = torch.max(inputs["pixel_values"]) + init_min = torch.min(inputs["pixel_values"]) - adv_art_x = attack._apply_perturbation(my_input[0:], perturbation, attack.eps_step) + mins = torch.tensor(original_image - 0.3).float().to(device) + maxs = torch.tensor(original_image + 0.3).float().to(device) - return perturbation, adv_art_x["pixel_values"].cpu().detach().numpy() + inputs["pixel_values"] = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) + pixel_values = torch.clamp(inputs["pixel_values"], min=mins, max=maxs) - def manual_attack(): + return sign.cpu().detach().numpy(), pixel_values.cpu().detach().numpy() - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - model = model.to(device) - inputs, original_image, labels, num_classes = get_and_process_input(return_batch=to_batch) - lossfn = torch.nn.CrossEntropyLoss() - inputs = inputs.to(device) + manual_pert, manual_sample = manual_attack() + perturbation, current_x = attack_clip() - inputs["pixel_values"] = inputs["pixel_values"].requires_grad_(True) + assert np.allclose(perturbation, manual_pert) + assert np.allclose(manual_sample, current_x) + except ARTTestException as e: + art_warning(e) - outputs = model(**inputs) - loss = lossfn(outputs.logits_per_image, labels.to(device)) - loss.backward() - sign = torch.sign(inputs["pixel_values"].grad) - init_max = torch.max(inputs["pixel_values"]) - init_min = torch.min(inputs["pixel_values"]) +@pytest.mark.only_with_platform("huggingface") +@pytest.mark.parametrize("to_one_hot", [False, True]) +def test_attack_functionality(art_warning, to_one_hot): + """ + Check that the attack results in valid adversarial examples which evade the model. + """ + try: + import torch - mins = torch.tensor(original_image - 0.3).float().to(device) - maxs = torch.tensor(original_image + 0.3).float().to(device) + from transformers import CLIPProcessor, CLIPModel - inputs["pixel_values"] = torch.clamp(inputs["pixel_values"] + sign * 0.1, min=init_min, max=init_max) - pixel_values = torch.clamp(inputs["pixel_values"], min=mins, max=maxs) + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) + from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy - return sign.cpu().detach().numpy(), pixel_values.cpu().detach().numpy() + std = np.asarray([0.26862954, 0.26130258, 0.27577711]) - manual_pert, manual_sample = manual_attack() - perturbation, current_x = attack_clip() + def norm_bound_eps(eps_bound=None): + if eps_bound is None: + eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) + eps_bound = np.abs(eps_bound / std) + return eps_bound - assert np.allclose(perturbation, manual_pert) - assert np.allclose(manual_sample, current_x) + fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/") + text = [ + "a photo of pink flowers", + "a photo of birds by the sea", + "a photo of a forest", + "a photo of a fern", + "a photo of a bus", + ] -def test_attack_functionality(): + input_list = [] + for fname in ["flowers", "birds", "forest", "ferns"]: + image = np.load(os.path.join(fpath, fname + ".npy")) + input_list.append(image) - import torch + labels = torch.tensor(np.asarray([0, 1, 2, 3])) - from transformers import CLIPProcessor, CLIPModel + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMultiModalPyTorch, - HuggingFaceMultiModalInput, - ) - from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy + loss_fn = torch.nn.CrossEntropyLoss() + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_images = [] + for i in range(len(labels)): + original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) + + original_images = np.stack(original_images) + + art_classifier = HuggingFaceMultiModalPyTorch( + model, + loss=loss_fn, + clip_values=(np.min(original_images), np.max(original_images)), + input_shape=(3, 224, 224), + ) - std = np.asarray([0.26862954, 0.26130258, 0.27577711]) + my_input = HuggingFaceMultiModalInput(**inputs) + clean_preds = art_classifier.predict(my_input) + clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - def norm_bound_eps(eps_bound=None): - if eps_bound is None: - eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255]) - eps_bound = np.abs(eps_bound / std) - return eps_bound + attack = CLIPProjectedGradientDescentNumpy( + art_classifier, + max_iter=10, + eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), + eps_step=np.ones((3, 224, 224)) * 0.1, + ) + x_adv = attack.generate(my_input, labels) + adv_preds = art_classifier.predict(x_adv) + adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/") + x_adv = x_adv["pixel_values"].cpu().detach().numpy() - text = [ - "a photo of pink flowers", - "a photo of birds by the sea", - "a photo of a forest", - "a photo of a fern", - "a photo of a bus", - ] + # Assert valid adversarial examples + assert np.all(x_adv >= np.min(original_images)) + assert np.all(x_adv <= np.max(original_images)) - input_list = [] - for fname in ["flowers", "birds", "forest", "ferns"]: - image = np.load(os.path.join(fpath, fname + ".npy")) - input_list.append(image) + eps_mins = original_images - np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)) + eps_maxs = original_images + np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)) - labels = torch.tensor(np.asarray([0, 1, 2, 3])) + eps_mins = eps_mins.flatten() + eps_maxs = eps_maxs.flatten() + x_adv = x_adv.flatten() - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + assert np.all(np.logical_or(x_adv >= eps_mins, np.isclose(x_adv, eps_mins))) + assert np.all(np.logical_or(x_adv <= eps_maxs, np.isclose(x_adv, eps_maxs))) - loss_fn = torch.nn.CrossEntropyLoss() - inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) - original_images = [] - for i in range(len(labels)): - original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) - - original_images = np.stack(original_images) - - art_classifier = HuggingFaceMultiModalPyTorch( - model, - loss=loss_fn, - clip_values=(np.min(original_images), np.max(original_images)), - input_shape=(3, 224, 224), - ) - - my_input = HuggingFaceMultiModalInput(**inputs) - clean_preds = art_classifier.predict(my_input) - clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - - attack = CLIPProjectedGradientDescentNumpy( - art_classifier, - max_iter=10, - eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)), - eps_step=np.ones((3, 224, 224)) * 0.1, - ) - x_adv = attack.generate(my_input, labels) - adv_preds = art_classifier.predict(x_adv) - adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels) - - x_adv = x_adv["pixel_values"].cpu().detach().numpy() - - # Assert valid adversarial examples - assert np.all(x_adv >= np.min(original_images)) - assert np.all(x_adv <= np.max(original_images)) - - eps_mins = original_images - np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)) - eps_maxs = original_images + np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)) - - eps_mins = eps_mins.flatten() - eps_maxs = eps_maxs.flatten() - x_adv = x_adv.flatten() - - assert np.all(np.logical_or(x_adv >= eps_mins, np.isclose(x_adv, eps_mins))) - assert np.all(np.logical_or(x_adv <= eps_maxs, np.isclose(x_adv, eps_maxs))) - - assert clean_acc == 1.0 - assert adv_acc == 0.0 + assert clean_acc == 1.0 + assert adv_acc == 0.0 + except ARTTestException as e: + art_warning(e) diff --git a/tests/estimators/classification/test_multimodal.py b/tests/estimators/classification/test_multimodal.py index 762e671217..1b442c1475 100644 --- a/tests/estimators/classification/test_multimodal.py +++ b/tests/estimators/classification/test_multimodal.py @@ -3,6 +3,7 @@ import pytest from art.utils import load_dataset +from tests.utils import ARTTestException @pytest.fixture() @@ -21,90 +22,91 @@ def fix_get_cifar10_data(): return x_test.astype(np.float32), y_test -def get_and_process_input(to_one_hot=False, return_batch=False): - - import torch - from transformers import CLIPProcessor - - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - text = ["a photo of pink flowers", "a photo of a dog", "a photo of a bear"] - - fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/flowers.npy") - - image = np.load(fpath) +@pytest.mark.only_with_platform("huggingface") +def test_predict(art_warning): + """ + Assert predictions function as expected. + """ + try: + import torch + from transformers import CLIPModel, CLIPProcessor + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + + fpath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "../../utils/data/images/") + + text = [ + "a photo of pink flowers", + "a photo of birds by the sea", + "a photo of a forest", + "a photo of a fern", + "a photo of a bus", + ] - if return_batch: input_list = [] - for _ in range(10): + for fname in ["flowers", "birds", "forest", "ferns"]: + image = np.load(os.path.join(fpath, fname + ".npy")) input_list.append(image) - inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) - original_image = inputs["pixel_values"][0].clone().cpu().numpy() - if to_one_hot: - labels = np.zeros((10, 3)) - labels = labels[0:10] + 1 - else: - labels = np.zeros((10,)) - labels = torch.tensor(labels).type(torch.LongTensor) - - else: + labels = np.asarray([0, 1, 2, 3]) + inputs = processor(text=text, images=input_list, return_tensors="pt", padding=True) + original_images = [] + for i in range(len(labels)): + original_images.append(inputs["pixel_values"][i].clone().cpu().detach().numpy()) - inputs = processor(text=text, images=image, return_tensors="pt", padding=True) - original_image = inputs.pixel_values.clone().cpu().numpy() - labels = torch.tensor(np.asarray([0])) + original_images = np.stack(original_images) - return inputs, original_image, labels, len(text) + art_classifier = HuggingFaceMultiModalPyTorch( + model, + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_images), np.max(original_images)), + input_shape=(3, 224, 224), + ) + inputs = HuggingFaceMultiModalInput(**inputs) + predictions = art_classifier.predict(inputs) + assert ((np.sum(np.argmax(predictions, axis=1) == labels) / len(labels)) == 1.0) + except ARTTestException as e: + art_warning(e) @pytest.mark.only_with_platform("huggingface") -def test_predict(): - import torch - from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMultiModalPyTorch, - HuggingFaceMultiModalInput, - ) - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - inputs, original_image, labels, num_classes = get_and_process_input(return_batch=True) - - art_classifier = HuggingFaceMultiModalPyTorch( - model, - loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - inputs = HuggingFaceMultiModalInput(**inputs) - _ = art_classifier.predict(inputs) - - -@pytest.mark.only_with_platform("huggingface") -def test_fit(fix_get_cifar10_data): - import torch - from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( - HuggingFaceMultiModalPyTorch, - HuggingFaceMultiModalInput, - ) - - x_train = fix_get_cifar10_data[0] - y_train = fix_get_cifar10_data[1] - - model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") - processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - - text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] - inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) - original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() - - inputs = HuggingFaceMultiModalInput(**inputs) - optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) - art_classifier = HuggingFaceMultiModalPyTorch( - model, - optimizer=optimizer, - loss=torch.nn.CrossEntropyLoss(), - clip_values=(np.min(original_image), np.max(original_image)), - input_shape=(3, 224, 224), - ) - - art_classifier.fit(inputs, y_train, nb_epochs=1) +def test_fit(art_warning, fix_get_cifar10_data): + """ + Assert training loop executes. + """ + try: + import torch + from transformers import CLIPProcessor, CLIPModel + from art.experimental.estimators.huggingface_multimodal import ( + HuggingFaceMultiModalPyTorch, + HuggingFaceMultiModalInput, + ) + + x_train = fix_get_cifar10_data[0] + y_train = fix_get_cifar10_data[1] + + model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + + text = ["plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] + inputs = processor(text=text, images=x_train, return_tensors="pt", padding=True) + original_image = inputs["pixel_values"][0].clone().cpu().detach().numpy() + + inputs = HuggingFaceMultiModalInput(**inputs) + optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + art_classifier = HuggingFaceMultiModalPyTorch( + model, + optimizer=optimizer, + loss=torch.nn.CrossEntropyLoss(), + clip_values=(np.min(original_image), np.max(original_image)), + input_shape=(3, 224, 224), + ) + + art_classifier.fit(inputs, y_train, nb_epochs=1) + except ARTTestException as e: + art_warning(e) From 9df9d14c29383a18e18aa5015ccc06a5b512ee00 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 30 Nov 2023 15:04:32 +0000 Subject: [PATCH 30/46] consistancy in naming Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 2 +- art/experimental/estimators/__init__.py | 2 +- .../hugging_face_multimodal/__init__.py | 5 +++++ .../hugging_face_mm.py | 10 +++++++--- .../hugging_face_mm_inputs.py | 0 .../huggingface_multimodal/__init__.py | 5 ----- notebooks/clip_attack.ipynb | 17 +++++------------ tests/attacks/evasion/test_multimodal_attack.py | 6 +++--- .../classification/test_multimodal.py | 6 +++--- 9 files changed, 25 insertions(+), 28 deletions(-) create mode 100644 art/experimental/estimators/hugging_face_multimodal/__init__.py rename art/experimental/estimators/{huggingface_multimodal => hugging_face_multimodal}/hugging_face_mm.py (97%) rename art/experimental/estimators/{huggingface_multimodal => hugging_face_multimodal}/hugging_face_mm_inputs.py (100%) delete mode 100644 art/experimental/estimators/huggingface_multimodal/__init__.py diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index ef12f9d3cb..496e240513 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -27,7 +27,7 @@ from art.attacks.evasion.fast_gradient import FastGradientMethod from art.attacks.attack import EvasionAttack from art.estimators.estimator import BaseEstimator, LossGradientsMixin -from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput +from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalInput from art.summary_writer import SummaryWriter from art.config import ART_NUMPY_DTYPE diff --git a/art/experimental/estimators/__init__.py b/art/experimental/estimators/__init__.py index 2cd257e783..693e6c884d 100644 --- a/art/experimental/estimators/__init__.py +++ b/art/experimental/estimators/__init__.py @@ -1,5 +1,5 @@ """ Experimental Estimator API """ -from art.experimental.estimators import huggingface_multimodal +from art.experimental.estimators import hugging_face_multimodal from art.experimental.estimators.jax import JaxEstimator diff --git a/art/experimental/estimators/hugging_face_multimodal/__init__.py b/art/experimental/estimators/hugging_face_multimodal/__init__.py new file mode 100644 index 0000000000..474a7c826b --- /dev/null +++ b/art/experimental/estimators/hugging_face_multimodal/__init__.py @@ -0,0 +1,5 @@ +""" +Module containing estimators for CLIP. +""" +from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm import HuggingFaceMultiModalPyTorch +from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput diff --git a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py similarity index 97% rename from art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py rename to art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py index 8c6d295fcf..aeca1d0c97 100644 --- a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py @@ -36,7 +36,7 @@ from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE from art.defences.preprocessor.preprocessor import Preprocessor from art.defences.postprocessor.postprocessor import Postprocessor - from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput + from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalInput logger = logging.getLogger(__name__) @@ -245,7 +245,9 @@ def predict( and providing it takes no effect. :return: Predictions over the supplied data. """ - from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput + from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import ( + HuggingFaceMultiModalInput, + ) # Set model to evaluation mode self._model.eval() @@ -289,7 +291,9 @@ def fit( # pylint: disable=W0221 and providing it takes no effect. """ import torch - from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput + from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import ( + HuggingFaceMultiModalInput, + ) self._model.train() if self._optimizer is None: diff --git a/art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py similarity index 100% rename from art/experimental/estimators/huggingface_multimodal/hugging_face_mm_inputs.py rename to art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py diff --git a/art/experimental/estimators/huggingface_multimodal/__init__.py b/art/experimental/estimators/huggingface_multimodal/__init__.py deleted file mode 100644 index 0cbbdaf570..0000000000 --- a/art/experimental/estimators/huggingface_multimodal/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Module containing estimators for CLIP. -""" -from art.experimental.estimators.huggingface_multimodal.hugging_face_mm import HuggingFaceMultiModalPyTorch -from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput diff --git a/notebooks/clip_attack.ipynb b/notebooks/clip_attack.ipynb index 4e50653e81..8d5ea7459a 100644 --- a/notebooks/clip_attack.ipynb +++ b/notebooks/clip_attack.ipynb @@ -22,7 +22,7 @@ "import numpy as np\n", "import torch\n", "\n", - "from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput\n", + "from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput\n", "from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy\n", "\n", "# Image normalization numbers\n", @@ -150,7 +150,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-11-30 09:48:11.240678: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "2023-11-30 14:58:29.132186: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, @@ -168,7 +168,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.70it/s]" + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.60it/s]" ] }, { @@ -188,7 +188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d733d074704040ec8eb12855556f4922", + "model_id": "04de95b8f8e244eea99333f888cdd862", "version_major": 2, "version_minor": 0 }, @@ -203,7 +203,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.95it/s]" + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2.15it/s]\n" ] }, { @@ -212,13 +212,6 @@ "text": [ "The adversarial accuracy is 0.0\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index bff239fcaf..a74d4eee75 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -46,7 +46,7 @@ def test_grad_equivalence(max_iter, art_warning): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( + from art.experimental.estimators.hugging_face_multimodal import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -103,7 +103,7 @@ def test_perturbation_equivalence(to_batch, art_warning): from transformers import CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( + from art.experimental.estimators.hugging_face_multimodal import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -183,7 +183,7 @@ def test_attack_functionality(art_warning, to_one_hot): from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( + from art.experimental.estimators.hugging_face_multimodal import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) diff --git a/tests/estimators/classification/test_multimodal.py b/tests/estimators/classification/test_multimodal.py index 1b442c1475..f786029e5f 100644 --- a/tests/estimators/classification/test_multimodal.py +++ b/tests/estimators/classification/test_multimodal.py @@ -30,7 +30,7 @@ def test_predict(art_warning): try: import torch from transformers import CLIPModel, CLIPProcessor - from art.experimental.estimators.huggingface_multimodal import ( + from art.experimental.estimators.hugging_face_multimodal import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -69,7 +69,7 @@ def test_predict(art_warning): ) inputs = HuggingFaceMultiModalInput(**inputs) predictions = art_classifier.predict(inputs) - assert ((np.sum(np.argmax(predictions, axis=1) == labels) / len(labels)) == 1.0) + assert (np.sum(np.argmax(predictions, axis=1) == labels) / len(labels)) == 1.0 except ARTTestException as e: art_warning(e) @@ -82,7 +82,7 @@ def test_fit(art_warning, fix_get_cifar10_data): try: import torch from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.huggingface_multimodal import ( + from art.experimental.estimators.hugging_face_multimodal import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) From 4bb41393af2f3af562b9bead38f76eaeae5b9d1d Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 30 Nov 2023 16:15:09 +0000 Subject: [PATCH 31/46] remove feature branch in ci pipeline Signed-off-by: GiulioZizzo --- .github/workflows/ci-huggingface.yml | 2 +- .github/workflows/ci-pytorch.yml | 1 - .github/workflows/ci-style-checks.yml | 1 - run_single_test.sh | 27 --------------------------- run_tests.sh | 3 ++- 5 files changed, 3 insertions(+), 31 deletions(-) delete mode 100644 run_single_test.sh diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml index a223b163e0..ed3056ad06 100644 --- a/.github/workflows/ci-huggingface.yml +++ b/.github/workflows/ci-huggingface.yml @@ -16,7 +16,7 @@ on: branches: - main - dev* - - clip_1.17_dev + # Run scheduled CI flow daily schedule: - cron: '0 8 * * 0' diff --git a/.github/workflows/ci-pytorch.yml b/.github/workflows/ci-pytorch.yml index ef231a5b1a..d162dfdcbd 100644 --- a/.github/workflows/ci-pytorch.yml +++ b/.github/workflows/ci-pytorch.yml @@ -16,7 +16,6 @@ on: branches: - main - dev* - - clip_attack # Run scheduled CI flow daily schedule: diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml index 549a4de552..c8283c8b9d 100644 --- a/.github/workflows/ci-style-checks.yml +++ b/.github/workflows/ci-style-checks.yml @@ -16,7 +16,6 @@ on: branches: - main - dev* - - clip_1.17_dev # Run scheduled CI flow daily schedule: diff --git a/run_single_test.sh b/run_single_test.sh deleted file mode 100644 index 64c81b0658..0000000000 --- a/run_single_test.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/usr/bin/env bash - -exit_code=0 - -# Set TensorFlow logging to minimum level ERROR -export TF_CPP_MIN_LOG_LEVEL="3" - -# --------------------------------------------------------------------------------------------------------------- TESTS - -# NOTE: All the tests should be ran within this loop. All other tests are legacy tests that must be made framework -# independent to be incorporated within this loop -frameworkList=("tensorflow" "keras" "pytorch" "scikitlearn" "mxnet" "kerastf") -framework=$1 -legacy_module=$2 - -if [[ ${framework} != "legacy" ]] -then - echo "#######################################################################" - echo "############### Running tests with framework $framework ###############" - echo "#######################################################################" - - pytest --cov-report=xml --cov=art --cov-append -q -vv tests/attacks/evasion/test_multimodal_attack.py --framework=$framework --durations=0 - if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed multimodal tests"; fi - -fi - -exit ${exit_code} diff --git a/run_tests.sh b/run_tests.sh index 101c8d2705..4b32ce7792 100755 --- a/run_tests.sh +++ b/run_tests.sh @@ -133,6 +133,7 @@ else "tests/estimators/regression/test_scikitlearn.py" ) declare -a defences=("tests/defences/test_adversarial_trainer.py" \ + "tests/defences/test_adversarial_trainer_madry_pgd.py" \ "tests/defences/test_class_labels.py" \ "tests/defences/test_defensive_distillation.py" \ "tests/defences/test_feature_squeezing.py" \ @@ -181,4 +182,4 @@ else done fi -exit ${exit_code} \ No newline at end of file +exit ${exit_code} From 48391d1ae6b454e57e635a088bda84f4826bc4e4 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 30 Nov 2023 19:19:11 +0000 Subject: [PATCH 32/46] mypy fixes Signed-off-by: GiulioZizzo --- art/experimental/attacks/evasion/fast_gradient.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 496e240513..f29517f244 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -155,8 +155,10 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes). :return: An array holding the adversarial examples. """ - adv_x = copy.deepcopy(x) + partial_stop_condition: Union[bool, np.ndarray, np.bool_] + current_eps: Union[int, float, np.ndarray] + adv_x = copy.deepcopy(x) # Compute perturbation with implicit batching for batch_id in range(int(np.ceil(adv_x.shape[0] / float(self.batch_size)))): batch_index_1, batch_index_2 = ( @@ -268,6 +270,10 @@ def _compute( decay: Optional[float] = None, momentum: Optional[np.ndarray] = None, ) -> np.ndarray: + + batch_eps: Union[int, float, np.ndarray] + batch_eps_step: Union[int, float, np.ndarray] + if random_init: n = x.shape[0] m = np.prod(x.shape[1:]).item() From 0defd9d4902b148c9953be2d26d000be890d8b89 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 09:29:14 +0000 Subject: [PATCH 33/46] mypy fixes Signed-off-by: GiulioZizzo --- art/experimental/estimators/__init__.py | 3 ++- .../estimators/hugging_face_multimodal/hugging_face_mm.py | 2 +- .../hugging_face_multimodal/hugging_face_mm_inputs.py | 7 ++++--- tests/attacks/evasion/test_multimodal_attack.py | 6 +++--- tests/estimators/classification/test_multimodal.py | 4 ++-- 5 files changed, 12 insertions(+), 10 deletions(-) diff --git a/art/experimental/estimators/__init__.py b/art/experimental/estimators/__init__.py index 693e6c884d..0ebc1afb83 100644 --- a/art/experimental/estimators/__init__.py +++ b/art/experimental/estimators/__init__.py @@ -1,5 +1,6 @@ """ Experimental Estimator API """ -from art.experimental.estimators import hugging_face_multimodal +from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm import HuggingFaceMultiModalPyTorch +from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput from art.experimental.estimators.jax import JaxEstimator diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py index aeca1d0c97..e202581c7d 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py @@ -302,7 +302,7 @@ def fit( # pylint: disable=W0221 y_tensor = torch.from_numpy(y) num_batch = int(len(y_tensor) / float(batch_size)) - ind = np.arange(len(y_tensor)) + ind = np.arange(len(y_tensor)).tolist() # Start training for _ in tqdm(range(nb_epochs)): diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py index ecec01d7f8..12144ee993 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py @@ -99,15 +99,16 @@ def __getitem__( :param item: Item to get. If accessing via array like functionality (slice, int, etc) pixel_values are fetched. Else, if passing a string will fetch like a ordinary dictionary """ - if isinstance(item, (slice, tuple, int, np.ndarray)): + if isinstance(item, (list, slice, tuple, int, np.ndarray)): pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values = pixel_values[item] output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output - if item in self.keys(): + elif item in self.keys(): return UserDict.__getitem__(self, item) - raise ValueError("Unsupported item for __getitem__ in HuggingFaceMultiModalInput") + else: + raise ValueError("Unsupported item for __getitem__ in HuggingFaceMultiModalInput") def __add__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> HuggingFaceMultiModalInput: """ diff --git a/tests/attacks/evasion/test_multimodal_attack.py b/tests/attacks/evasion/test_multimodal_attack.py index a74d4eee75..02ec441f6a 100644 --- a/tests/attacks/evasion/test_multimodal_attack.py +++ b/tests/attacks/evasion/test_multimodal_attack.py @@ -46,7 +46,7 @@ def test_grad_equivalence(max_iter, art_warning): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from transformers import CLIPModel - from art.experimental.estimators.hugging_face_multimodal import ( + from art.experimental.estimators import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -103,7 +103,7 @@ def test_perturbation_equivalence(to_batch, art_warning): from transformers import CLIPModel - from art.experimental.estimators.hugging_face_multimodal import ( + from art.experimental.estimators import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -183,7 +183,7 @@ def test_attack_functionality(art_warning, to_one_hot): from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.hugging_face_multimodal import ( + from art.experimental.estimators import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) diff --git a/tests/estimators/classification/test_multimodal.py b/tests/estimators/classification/test_multimodal.py index f786029e5f..f77c5ea512 100644 --- a/tests/estimators/classification/test_multimodal.py +++ b/tests/estimators/classification/test_multimodal.py @@ -30,7 +30,7 @@ def test_predict(art_warning): try: import torch from transformers import CLIPModel, CLIPProcessor - from art.experimental.estimators.hugging_face_multimodal import ( + from art.experimental.estimators import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) @@ -82,7 +82,7 @@ def test_fit(art_warning, fix_get_cifar10_data): try: import torch from transformers import CLIPProcessor, CLIPModel - from art.experimental.estimators.hugging_face_multimodal import ( + from art.experimental.estimators import ( HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput, ) From 0b5b773cac756c84506631eea04aacae7deb028b Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 11:18:26 +0000 Subject: [PATCH 34/46] checking codeql error Signed-off-by: GiulioZizzo --- .github/workflows/codeql-analysis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index 91066aeb9c..4021614d35 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -7,7 +7,7 @@ name: "CodeQL" on: push: - branches: [main, dev_*] + branches: [main, dev_*, clip_1.17_dev] pull_request: # The branches below must be a subset of the branches above branches: [main, dev_*] From e8e474610e50cfa333eabd43145eca8d274592ac Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 11:33:15 +0000 Subject: [PATCH 35/46] Formatting fix. Check if deepcopy is the problem with codeQL Signed-off-by: GiulioZizzo --- art/experimental/attacks/evasion/fast_gradient.py | 2 +- .../hugging_face_multimodal/hugging_face_mm_inputs.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index f29517f244..fbdd856452 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -287,7 +287,7 @@ def _compute( x_adv = np.clip(x_adv, clip_min, clip_max) else: if x.dtype == object: - x_adv = copy.deepcopy(x) + x_adv = copy.copy(x) else: x_adv = x.astype(ART_NUMPY_DTYPE) diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py index 12144ee993..7f337dbf01 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py @@ -107,8 +107,7 @@ def __getitem__( return output elif item in self.keys(): return UserDict.__getitem__(self, item) - else: - raise ValueError("Unsupported item for __getitem__ in HuggingFaceMultiModalInput") + raise ValueError("Unsupported item for __getitem__ in HuggingFaceMultiModalInput") def __add__(self, other: Union[HuggingFaceMultiModalInput, np.ndarray]) -> HuggingFaceMultiModalInput: """ From c7573eea4a81138d17aeae9963c955ac9e9158b3 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 11:55:29 +0000 Subject: [PATCH 36/46] check sentinel fix Signed-off-by: GiulioZizzo --- art/experimental/attacks/evasion/fast_gradient.py | 7 +++++-- .../hugging_face_multimodal/hugging_face_mm_inputs.py | 2 +- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index fbdd856452..d6fefcec50 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -157,6 +157,7 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) """ partial_stop_condition: Union[bool, np.ndarray, np.bool_] current_eps: Union[int, float, np.ndarray] + sentinel = object() adv_x = copy.deepcopy(x) # Compute perturbation with implicit batching @@ -165,7 +166,9 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) batch_id * self.batch_size, (batch_id + 1) * self.batch_size, ) - batch = adv_x[batch_index_1:batch_index_2] + batch = sentinel + if batch is sentinel: + batch = adv_x[batch_index_1:batch_index_2] batch_labels = y[batch_index_1:batch_index_2] mask_batch = mask @@ -287,7 +290,7 @@ def _compute( x_adv = np.clip(x_adv, clip_min, clip_max) else: if x.dtype == object: - x_adv = copy.copy(x) + x_adv = copy.deepcopy(x) else: x_adv = x.astype(ART_NUMPY_DTYPE) diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py index 7f337dbf01..d10ab8510d 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py @@ -105,7 +105,7 @@ def __getitem__( output = HuggingFaceMultiModalInput(**self) output["pixel_values"] = pixel_values return output - elif item in self.keys(): + if item in self.keys(): return UserDict.__getitem__(self, item) raise ValueError("Unsupported item for __getitem__ in HuggingFaceMultiModalInput") From 8bbf92cc6dbc6639e7a016182fd0f7110a0476f3 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 12:12:30 +0000 Subject: [PATCH 37/46] refactor to address codeQL Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index d6fefcec50..69634c2c4e 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -166,9 +166,9 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) batch_id * self.batch_size, (batch_id + 1) * self.batch_size, ) - batch = sentinel - if batch is sentinel: - batch = adv_x[batch_index_1:batch_index_2] + # batch = sentinel + # if batch is sentinel: + # batch = adv_x[batch_index_1:batch_index_2] batch_labels = y[batch_index_1:batch_index_2] mask_batch = mask @@ -179,10 +179,10 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) mask_batch = mask[batch_index_1:batch_index_2] # Get perturbation - perturbation = self._compute_perturbation(batch, batch_labels, mask_batch) + perturbation = self._compute_perturbation(adv_x[batch_index_1:batch_index_2], batch_labels, mask_batch) # Get current predictions - active_indices = np.arange(len(batch)) + active_indices = np.arange(len(adv_x[batch_index_1:batch_index_2])) + batch_index_1 if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray): if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]: @@ -202,14 +202,14 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) current_x = self._apply_perturbation(x[batch_index_1:batch_index_2], perturbation, current_eps) # Update - batch[active_indices] = current_x[active_indices] - adv_preds = self.estimator.predict(batch) + adv_x[active_indices] = current_x[active_indices] + adv_preds = self.estimator.predict(adv_x[batch_index_1:batch_index_2]) # If targeted active check to see whether we have hit the target, otherwise head to anything but if self.targeted: - active_indices = np.where(np.argmax(batch_labels, axis=1) != np.argmax(adv_preds, axis=1))[0] + active_indices = np.where(np.argmax(batch_labels, axis=1) != np.argmax(adv_preds, axis=1))[0] + batch_index_1 else: - active_indices = np.where(np.argmax(batch_labels, axis=1) == np.argmax(adv_preds, axis=1))[0] + active_indices = np.where(np.argmax(batch_labels, axis=1) == np.argmax(adv_preds, axis=1))[0] + batch_index_1 # Update current eps and check the stop condition if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray): From ae9a2618999f5e87ef54d0762e064e6c0b6b25d5 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 12:48:19 +0000 Subject: [PATCH 38/46] refactor for codeQL Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 23 ++++++++++--------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 69634c2c4e..4982fca8c3 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -157,7 +157,6 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) """ partial_stop_condition: Union[bool, np.ndarray, np.bool_] current_eps: Union[int, float, np.ndarray] - sentinel = object() adv_x = copy.deepcopy(x) # Compute perturbation with implicit batching @@ -166,9 +165,6 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) batch_id * self.batch_size, (batch_id + 1) * self.batch_size, ) - # batch = sentinel - # if batch is sentinel: - # batch = adv_x[batch_index_1:batch_index_2] batch_labels = y[batch_index_1:batch_index_2] mask_batch = mask @@ -207,9 +203,13 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) adv_preds = self.estimator.predict(adv_x[batch_index_1:batch_index_2]) # If targeted active check to see whether we have hit the target, otherwise head to anything but if self.targeted: - active_indices = np.where(np.argmax(batch_labels, axis=1) != np.argmax(adv_preds, axis=1))[0] + batch_index_1 + active_indices = ( + np.where(np.argmax(batch_labels, axis=1) != np.argmax(adv_preds, axis=1))[0] + batch_index_1 + ) else: - active_indices = np.where(np.argmax(batch_labels, axis=1) == np.argmax(adv_preds, axis=1))[0] + batch_index_1 + active_indices = ( + np.where(np.argmax(batch_labels, axis=1) == np.argmax(adv_preds, axis=1))[0] + batch_index_1 + ) # Update current eps and check the stop condition if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray): @@ -225,8 +225,6 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) current_eps = current_eps + self.eps_step partial_stop_condition = current_eps <= self.eps - adv_x[batch_index_1:batch_index_2] = batch - return adv_x def _apply_perturbation( @@ -302,7 +300,6 @@ def _compute( self._batch_id = batch_id_ext batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size batch_index_2 = min(batch_index_2, x.shape[0]) - batch = x_adv[batch_index_1:batch_index_2] batch_labels = y[batch_index_1:batch_index_2] mask_batch = mask @@ -313,7 +310,9 @@ def _compute( mask_batch = mask[batch_index_1:batch_index_2] # Get perturbation - perturbation = self._compute_perturbation(batch, batch_labels, mask_batch, decay, momentum) + perturbation = self._compute_perturbation( + x_adv[batch_index_1:batch_index_2], batch_labels, mask_batch, decay, momentum + ) # Compute batch_eps and batch_eps_step if isinstance(eps, np.ndarray) and isinstance(eps_step, np.ndarray): @@ -330,7 +329,9 @@ def _compute( batch_eps_step = eps_step # Apply perturbation and clip - x_adv[batch_index_1:batch_index_2] = self._apply_perturbation(batch, perturbation, batch_eps_step) + x_adv[batch_index_1:batch_index_2] = self._apply_perturbation( + x_adv[batch_index_1:batch_index_2], perturbation, batch_eps_step + ) if project: if x_adv.dtype == object: From 455f31e976f695ffb9dcc80266f711528ddc011d Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 14:04:54 +0000 Subject: [PATCH 39/46] refactor for codeQL Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 4982fca8c3..3032d16e69 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -271,7 +271,7 @@ def _compute( decay: Optional[float] = None, momentum: Optional[np.ndarray] = None, ) -> np.ndarray: - + import torch batch_eps: Union[int, float, np.ndarray] batch_eps_step: Union[int, float, np.ndarray] @@ -293,6 +293,7 @@ def _compute( x_adv = x.astype(ART_NUMPY_DTYPE) # Compute perturbation with implicit batching + x_adv_result = [] for batch_id in range(int(np.ceil(x.shape[0] / float(self.batch_size)))): if batch_id_ext is None: self._batch_id = batch_id @@ -329,7 +330,7 @@ def _compute( batch_eps_step = eps_step # Apply perturbation and clip - x_adv[batch_index_1:batch_index_2] = self._apply_perturbation( + x_adv_batch = self._apply_perturbation( x_adv[batch_index_1:batch_index_2], perturbation, batch_eps_step ) @@ -338,20 +339,22 @@ def _compute( for i_sample in range(batch_index_1, batch_index_2): if isinstance(batch_eps, np.ndarray) and batch_eps.shape[0] == x_adv.shape[0]: perturbation = multimodal_projection( - x_adv[i_sample] - x_init[i_sample], batch_eps[i_sample], self.norm + x_adv_batch[i_sample - batch_index_1] - x_init[i_sample], batch_eps[i_sample], self.norm ) else: perturbation = multimodal_projection( - x_adv[i_sample] - x_init[i_sample], batch_eps, self.norm + x_adv_batch[i_sample - batch_index_1] - x_init[i_sample], batch_eps, self.norm ) - x_adv[i_sample] = x_init[i_sample] + perturbation + x_adv_batch[i_sample - batch_index_1] = x_init[i_sample] + perturbation else: perturbation = multimodal_projection( - x_adv[batch_index_1:batch_index_2] - x_init[batch_index_1:batch_index_2], batch_eps, self.norm + x_adv_batch - x_init[batch_index_1:batch_index_2], batch_eps, self.norm ) - x_adv[batch_index_1:batch_index_2] = x_init[batch_index_1:batch_index_2] + perturbation - + x_adv_batch = x_init[batch_index_1:batch_index_2] + perturbation + x_adv_result.append(x_adv_batch) + x_adv_result = torch.stack(x_adv_result) + x_adv['pixel_values'] = x_adv_result return x_adv From fc37e879e5578c11baef3e8301111e3496fa3169 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 14:22:32 +0000 Subject: [PATCH 40/46] refactor for codeQL Signed-off-by: GiulioZizzo --- art/experimental/attacks/evasion/fast_gradient.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 3032d16e69..05f1be1f26 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -274,6 +274,7 @@ def _compute( import torch batch_eps: Union[int, float, np.ndarray] batch_eps_step: Union[int, float, np.ndarray] + original_type = x['pixel_values'].dtype if random_init: n = x.shape[0] @@ -354,7 +355,9 @@ def _compute( x_adv_batch - x_init[batch_index_1:batch_index_2], batch_eps, self.norm ) x_adv_batch = x_init[batch_index_1:batch_index_2] + perturbation - x_adv_result.append(x_adv_batch) - x_adv_result = torch.stack(x_adv_result) - x_adv['pixel_values'] = x_adv_result + x_adv_result.append(x_adv_batch['pixel_values']) + + x_adv_result = torch.concatenate(x_adv_result) + x_adv['pixel_values'] = x_adv_result.type(original_type) + return x_adv From 689777ec884e412d8922cd0289b4bd708b1e677d Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 15:10:50 +0000 Subject: [PATCH 41/46] try sentinel fix Signed-off-by: GiulioZizzo --- art/experimental/attacks/evasion/fast_gradient.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 05f1be1f26..48a6b2d9ac 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -358,6 +358,12 @@ def _compute( x_adv_result.append(x_adv_batch['pixel_values']) x_adv_result = torch.concatenate(x_adv_result) - x_adv['pixel_values'] = x_adv_result.type(original_type) + sentinel = object() - return x_adv + def myfunc(adv, x_sample=sentinel): + if x_sample is sentinel: + x_sample = x_adv + x_sample['pixel_values'] = adv.type(original_type) + return x_sample + + return myfunc(x_adv_result, x_sample=x_adv) From 5a92140280d1b2d0d15ccb02738d5b7da55b5bed Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 15:26:45 +0000 Subject: [PATCH 42/46] refactor with setter method for codeQL Signed-off-by: GiulioZizzo --- art/experimental/attacks/evasion/fast_gradient.py | 10 +--------- .../hugging_face_multimodal/hugging_face_mm_inputs.py | 3 +++ 2 files changed, 4 insertions(+), 9 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 48a6b2d9ac..5278cadf5a 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -358,12 +358,4 @@ def _compute( x_adv_result.append(x_adv_batch['pixel_values']) x_adv_result = torch.concatenate(x_adv_result) - sentinel = object() - - def myfunc(adv, x_sample=sentinel): - if x_sample is sentinel: - x_sample = x_adv - x_sample['pixel_values'] = adv.type(original_type) - return x_sample - - return myfunc(x_adv_result, x_sample=x_adv) + return x_adv.update_pixels(x_adv_result) diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py index d10ab8510d..e3f9c763b4 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py @@ -184,6 +184,9 @@ def __len__(self) -> int: pixel_values = UserDict.__getitem__(self, "pixel_values") return len(pixel_values) + def update_pixels(self, pixel_values: torch.Tensor) -> None: + super().__setitem__("pixel_values", pixel_values) + def reshape(self, new_shape: Tuple) -> HuggingFaceMultiModalInput: """ Defines reshaping on the HuggingFaceMultiModalInput input. From 105c881d017b32f38797d1c1a847279d9683b68b Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 17:58:46 +0000 Subject: [PATCH 43/46] refactor for codeQl fix Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 28 +++++++++++++------ .../hugging_face_mm_inputs.py | 17 +++++++++-- 2 files changed, 35 insertions(+), 10 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 5278cadf5a..85749dcb90 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -20,7 +20,7 @@ """ import copy from collections import UserDict -from typing import Optional, Union, TYPE_CHECKING +from typing import List, Optional, Union, TYPE_CHECKING import numpy as np @@ -198,7 +198,10 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) current_x = self._apply_perturbation(x[batch_index_1:batch_index_2], perturbation, current_eps) # Update - adv_x[active_indices] = current_x[active_indices] + if isinstance(adv_x, HuggingFaceMultiModalInput): + adv_x[active_indices] = current_x[active_indices] + else: + raise ValueError("Compatibility supported for HF style inputs") adv_preds = self.estimator.predict(adv_x[batch_index_1:batch_index_2]) # If targeted active check to see whether we have hit the target, otherwise head to anything but @@ -274,7 +277,10 @@ def _compute( import torch batch_eps: Union[int, float, np.ndarray] batch_eps_step: Union[int, float, np.ndarray] - original_type = x['pixel_values'].dtype + if isinstance(x, HuggingFaceMultiModalInput): + original_type = x['pixel_values'].dtype + else: + original_type = x.dtype if random_init: n = x.shape[0] @@ -294,7 +300,8 @@ def _compute( x_adv = x.astype(ART_NUMPY_DTYPE) # Compute perturbation with implicit batching - x_adv_result = [] + x_adv_result_list: List[torch.Tensor] = [] + x_adv_np_result_list: List[np.ndarray] = [] for batch_id in range(int(np.ceil(x.shape[0] / float(self.batch_size)))): if batch_id_ext is None: self._batch_id = batch_id @@ -355,7 +362,12 @@ def _compute( x_adv_batch - x_init[batch_index_1:batch_index_2], batch_eps, self.norm ) x_adv_batch = x_init[batch_index_1:batch_index_2] + perturbation - x_adv_result.append(x_adv_batch['pixel_values']) - - x_adv_result = torch.concatenate(x_adv_result) - return x_adv.update_pixels(x_adv_result) + if isinstance(x_adv, HuggingFaceMultiModalInput): + x_adv_result_list.append(x_adv_batch['pixel_values']) + if isinstance(x_adv, np.ndarray): + x_adv_np_result_list.append(x_adv_batch) + + if isinstance(original_type, str) or isinstance(original_type, torch.dtype): + x_adv_result = torch.concatenate(x_adv_result_list).type(original_type) + return x_adv.update_pixels(x_adv_result) # type: ignore + return np.concatenate(x_adv_np_result_list) diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py index e3f9c763b4..8f4d484382 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py @@ -184,8 +184,21 @@ def __len__(self) -> int: pixel_values = UserDict.__getitem__(self, "pixel_values") return len(pixel_values) - def update_pixels(self, pixel_values: torch.Tensor) -> None: - super().__setitem__("pixel_values", pixel_values) + def update_pixels(self, updated_pixel_values: torch.Tensor, + indices: Optional[np.ndarray] = None) -> HuggingFaceMultiModalInput: + """ + Helper method to set pixel values + :param updated_pixel_values: pixel values to set. + :param indices: If to partially update the values based on indices + """ + if indices is None: + super().__setitem__("pixel_values", updated_pixel_values) + else: + indices_list = indices.tolist() + pixel_values = UserDict.__getitem__(self, "pixel_values") + pixel_values[indices_list] = updated_pixel_values[indices_list] + super().__setitem__("pixel_values", pixel_values) + return self def reshape(self, new_shape: Tuple) -> HuggingFaceMultiModalInput: """ From 31cbcca35ade684d145aeff9a9c7631e62dbb2ee Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 1 Dec 2023 18:45:49 +0000 Subject: [PATCH 44/46] refactor for codeQl fix Signed-off-by: GiulioZizzo --- .../attacks/evasion/fast_gradient.py | 16 ++++++++-------- .../hugging_face_mm_inputs.py | 7 +++---- 2 files changed, 11 insertions(+), 12 deletions(-) diff --git a/art/experimental/attacks/evasion/fast_gradient.py b/art/experimental/attacks/evasion/fast_gradient.py index 85749dcb90..5cc6e07f17 100644 --- a/art/experimental/attacks/evasion/fast_gradient.py +++ b/art/experimental/attacks/evasion/fast_gradient.py @@ -199,7 +199,7 @@ def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) # Update if isinstance(adv_x, HuggingFaceMultiModalInput): - adv_x[active_indices] = current_x[active_indices] + adv_x.update_pixels(current_x, active_indices) # type: ignore else: raise ValueError("Compatibility supported for HF style inputs") @@ -275,10 +275,11 @@ def _compute( momentum: Optional[np.ndarray] = None, ) -> np.ndarray: import torch + batch_eps: Union[int, float, np.ndarray] batch_eps_step: Union[int, float, np.ndarray] if isinstance(x, HuggingFaceMultiModalInput): - original_type = x['pixel_values'].dtype + original_type = x["pixel_values"].dtype else: original_type = x.dtype @@ -338,9 +339,7 @@ def _compute( batch_eps_step = eps_step # Apply perturbation and clip - x_adv_batch = self._apply_perturbation( - x_adv[batch_index_1:batch_index_2], perturbation, batch_eps_step - ) + x_adv_batch = self._apply_perturbation(x_adv[batch_index_1:batch_index_2], perturbation, batch_eps_step) if project: if x_adv.dtype == object: @@ -363,11 +362,12 @@ def _compute( ) x_adv_batch = x_init[batch_index_1:batch_index_2] + perturbation if isinstance(x_adv, HuggingFaceMultiModalInput): - x_adv_result_list.append(x_adv_batch['pixel_values']) + x_adv_result_list.append(x_adv_batch["pixel_values"]) if isinstance(x_adv, np.ndarray): x_adv_np_result_list.append(x_adv_batch) - if isinstance(original_type, str) or isinstance(original_type, torch.dtype): + if isinstance(original_type, (str, torch.dtype)): x_adv_result = torch.concatenate(x_adv_result_list).type(original_type) - return x_adv.update_pixels(x_adv_result) # type: ignore + x_adv.update_pixels(x_adv_result) # type: ignore + return x_adv return np.concatenate(x_adv_np_result_list) diff --git a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py index 8f4d484382..3d35f52064 100644 --- a/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py +++ b/art/experimental/estimators/hugging_face_multimodal/hugging_face_mm_inputs.py @@ -184,12 +184,12 @@ def __len__(self) -> int: pixel_values = UserDict.__getitem__(self, "pixel_values") return len(pixel_values) - def update_pixels(self, updated_pixel_values: torch.Tensor, - indices: Optional[np.ndarray] = None) -> HuggingFaceMultiModalInput: + def update_pixels(self, updated_pixel_values: torch.Tensor, indices: Optional[np.ndarray] = None) -> None: """ Helper method to set pixel values :param updated_pixel_values: pixel values to set. :param indices: If to partially update the values based on indices + :return: The HuggingFaceMultiModalInput instance with updated pixel values """ if indices is None: super().__setitem__("pixel_values", updated_pixel_values) @@ -198,7 +198,6 @@ def update_pixels(self, updated_pixel_values: torch.Tensor, pixel_values = UserDict.__getitem__(self, "pixel_values") pixel_values[indices_list] = updated_pixel_values[indices_list] super().__setitem__("pixel_values", pixel_values) - return self def reshape(self, new_shape: Tuple) -> HuggingFaceMultiModalInput: """ @@ -237,4 +236,4 @@ def grad(): """ Enable mypy compatibility """ - raise ValueError("Trying to access is_leaf for the whole dictionary. Please use on individual tensors") + raise ValueError("Trying to access grad for the whole dictionary. Please use on individual tensors") From ea35d39ada35a3bbfbe7346f4116da5de2eac5b8 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Thu, 7 Dec 2023 18:18:15 +0000 Subject: [PATCH 45/46] explicitly removing random restarts as ART currently only supports restarts for classical classification tasks Signed-off-by: GiulioZizzo --- .../projected_gradient_descent_numpy.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py index 597d6cecbb..4e539999a2 100644 --- a/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py +++ b/art/experimental/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py @@ -60,7 +60,6 @@ def __init__( decay: Optional[float] = None, max_iter: int = 100, targeted: bool = False, - num_random_init: int = 0, batch_size: int = 32, random_eps: bool = False, summary_writer: Union[str, bool, SummaryWriter] = False, @@ -79,8 +78,6 @@ def __init__( PGD is untested (https://arxiv.org/pdf/1611.01236.pdf). :param max_iter: The maximum number of iterations. :param targeted: Indicates whether the attack is targeted (True) or untargeted (False) - :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting - at the original input. :param batch_size: Size of the batch on which adversarial samples are generated. :param summary_writer: Activate summary writer for TensorBoard. Default is `False` and deactivated summary writer. @@ -91,8 +88,6 @@ def __init__( ‘runs/exp1’, ‘runs/exp2’, etc. for each new experiment to compare across them. :param verbose: Show progress bars. """ - if summary_writer and num_random_init > 1: - raise ValueError("TensorBoard is not yet supported for more than 1 random restart (num_random_init>1).") super().__init__( estimator=estimator, @@ -102,7 +97,7 @@ def __init__( decay=decay, max_iter=max_iter, targeted=targeted, - num_random_init=num_random_init, + num_random_init=0, batch_size=batch_size, random_eps=random_eps, summary_writer=summary_writer, From 69519237a3e418ef42606fffd786bd0f1e928126 Mon Sep 17 00:00:00 2001 From: GiulioZizzo Date: Fri, 2 Feb 2024 14:56:22 +0000 Subject: [PATCH 46/46] updating notebook Signed-off-by: GiulioZizzo --- notebooks/clip_attack.ipynb | 441 ++++++++++++++++++++++++++---------- 1 file changed, 325 insertions(+), 116 deletions(-) diff --git a/notebooks/clip_attack.ipynb b/notebooks/clip_attack.ipynb index 8d5ea7459a..cb37c844cc 100644 --- a/notebooks/clip_attack.ipynb +++ b/notebooks/clip_attack.ipynb @@ -5,11 +5,11 @@ "id": "4e111634-8795-4707-b71e-9eb2df0eba78", "metadata": {}, "source": [ - "

Attacking CLIP for Image Classification

\n", + "# Attacking CLIP for Image Classification\n", + "\n", "In this notebook we show how to use the experimental tools in ART to attack the CLIP model.\n", "\n", - "CLIP is a multimodal foundation model able to handle both images and text.\n", - "Here we deomstrate how to attack the image recognition portion of CLIP so that it miscassifies a given input.\n" + "CLIP is a multimodal foundation model able to handle both images and text. Here we demonstrate how to attack the image recognition portion of CLIP so that it misclassifies a given input.\n" ] }, { @@ -17,165 +17,214 @@ "execution_count": 1, "id": "9da58be9-e228-4928-9f73-d146cbb3cc7a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/farhan/Downloads/adversarial-robustness-toolbox/venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + } + ], "source": [ + "import requests\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "import torch\n", + "from PIL import Image\n", + "from transformers import CLIPProcessor, CLIPModel\n", "\n", "from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput\n", - "from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy\n", - "\n", - "# Image normalization numbers\n", - "MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073])\n", - "STD = np.asarray([0.26862954, 0.26130258, 0.27577711])" + "from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy" ] }, { "cell_type": "code", "execution_count": 2, - "id": "a9fedf31-54cf-4615-aa30-c731f1b6ec25", + "id": "447db9ce", "metadata": {}, "outputs": [], "source": [ - "def get_data():\n", - " \"\"\"\n", - " We get sample data from the COCO dataset.\n", - " \"\"\"\n", - " from PIL import Image\n", - " import requests\n", - " \n", - " image_list = ['000000039769.jpg',\n", - " '000000000285.jpg',\n", - " '000000002006.jpg',\n", - " '000000002149.jpg']\n", - "\n", - " # Freetext description of the content of the classes we will try and sort the pictures into.\n", - " text = [\"a photo of a cat\", \"a photo of a bear\", \"a photo of a car\", \"a photo of a bus\", \"apples\"]\n", - "\n", - " # Ground truth labels mapping the images into one of the free-text categories. \n", - " # Note, we do not have an image of a car in this sample of data\n", - " labels = torch.tensor(np.asarray([0, 1, 3, 4]))\n", - "\n", - " input_list = []\n", - " for fname in image_list:\n", - " url = 'http://images.cocodataset.org/val2017/' + fname\n", - " input_list.append(Image.open(requests.get(url, stream=True).raw))\n", + "# Image normalization numbers\n", + "MEAN = np.asarray([0.48145466, 0.4578275, 0.40821073])\n", + "STD = np.asarray([0.26862954, 0.26130258, 0.27577711])" + ] + }, + { + "cell_type": "markdown", + "id": "13ab5d41", + "metadata": {}, + "source": [ + "## Load Data\n", "\n", - " return input_list, text, labels" + "We get sample data from the COCO dataset" ] }, { "cell_type": "code", "execution_count": 3, - "id": "c361f642-e60f-4aa1-8368-5b320fbc432d", + "id": "91538646", "metadata": {}, "outputs": [], "source": [ - "input_list, text, labels = get_data()" + "# Image IDs for images in the COCO dataset\n", + "image_list = ['000000039769.jpg', '000000000285.jpg', '000000002006.jpg', '000000002149.jpg']\n", + "\n", + "# Freetext description of the content of the classes we will try and sort the pictures into.\n", + "text = [\"a photo of a cat\", \"a photo of a bear\", \"a photo of a car\", \"a photo of a bus\", \"apples\"]\n", + "\n", + "# Ground truth labels mapping the images into one of the free-text categories. \n", + "# Note, we do not have an image of a car in this sample of data\n", + "labels = torch.tensor([0, 1, 3, 4])" ] }, { "cell_type": "code", "execution_count": 4, - "id": "baa53f63-eae8-44f0-ad97-df5d67d6119c", + "id": "aa9d918d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "def norm_bound_eps(eps_bound=None):\n", - " \"\"\"\n", - " Helper function to normalise the l_infinity bounds from 0 - 1 into z normalization.\n", - " \"\"\"\n", - " if eps_bound is None:\n", - " eps_bound = np.asarray([8 / 255, 8 / 255, 8 / 255])\n", - " eps_bound = np.abs(eps_bound / STD)\n", - " return eps_bound" + "images = []\n", + "for fname in image_list:\n", + " url = 'http://images.cocodataset.org/val2017/' + fname\n", + " images.append(Image.open(requests.get(url, stream=True).raw))\n", + "\n", + "images" ] }, { "cell_type": "code", "execution_count": 5, - "id": "557624e0-3446-4f74-bc8f-383783e49c9d", + "id": "8c405643", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "def attack(input_list, text, labels):\n", - " \"\"\"\n", - " We now attack the clip model by perturbing the input images using ARTs tools.\n", - " \"\"\"\n", - " from transformers import CLIPProcessor, CLIPModel\n", + "fig, ax = plt.subplots(1, 4, figsize=(10, 4))\n", + "fig.tight_layout()\n", + "for i, image in enumerate(images):\n", + " ax[i].imshow(image)\n", + " ax[i].axis('off')\n", + " ax[i].set_title(text[labels[i]])\n", "\n", - " model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n", - " processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n", - "\n", - " loss_fn = torch.nn.CrossEntropyLoss()\n", - " inputs = processor(text=text, images=input_list, return_tensors=\"pt\", padding=True)\n", - " original_images = []\n", - " for i in range(3):\n", - " original_images.append(inputs[\"pixel_values\"][i].clone().cpu().detach().numpy())\n", - " original_images = np.concatenate(original_images)\n", - "\n", - " art_classifier = HuggingFaceMultiModalPyTorch(\n", - " model, \n", - " loss=loss_fn,\n", - " clip_values=(np.min(original_images), np.max(original_images)), \n", - " input_shape=(3, 224, 224)\n", - " )\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f1717905", + "metadata": {}, + "source": [ + "## Load Model\n", "\n", - " art_input = HuggingFaceMultiModalInput(**inputs)\n", - " clean_preds = art_classifier.predict(art_input)\n", + "We will be using a CLIP model from Hugging Face that uses a ViT-B/32 Transformer as the image encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a581f297", + "metadata": {}, + "outputs": [], + "source": [ + "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n", + "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "373ecf77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 3, 224, 224])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inputs = processor(text=text, images=images, return_tensors=\"pt\", padding=True)\n", "\n", - " clean_acc = np.sum(np.argmax(clean_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels)\n", - " print('The clean accuracy is ', clean_acc)\n", + "inputs[\"pixel_values\"].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "aa787be4", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = torch.nn.CrossEntropyLoss()\n", "\n", - " attack = CLIPProjectedGradientDescentNumpy(\n", - " art_classifier,\n", - " max_iter=10,\n", - " eps=np.ones((3, 224, 224)) * np.reshape(norm_bound_eps(), (3, 1, 1)),\n", - " eps_step=np.ones((3, 224, 224)) * 0.1,\n", - " )\n", - " x_adv = attack.generate(art_input, labels)\n", - " adv_preds = art_classifier.predict(x_adv)\n", - " adv_acc = np.sum(np.argmax(adv_preds, axis=1) == labels.cpu().detach().numpy()) / len(labels)\n", + "classifier = HuggingFaceMultiModalPyTorch(\n", + " model,\n", + " loss=loss_fn,\n", + " clip_values=(inputs['pixel_values'].min(), inputs['pixel_values'].max()),\n", + " input_shape=(3, 224, 224)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "48cab930", + "metadata": {}, + "source": [ + "## Evaluate Model\n", "\n", - " print('The adversarial accuracy is ', adv_acc)\n" + "We first evaluate this model on clean data." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "5f011a60-2381-4d3f-866a-a39ae2279dde", + "execution_count": 10, + "id": "563a6600", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-30 14:58:29.132186: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.60it/s]" + "100%|██████████| 1/1 [00:00<00:00, 9.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The clean accuracy is 1.0\n" + "The clean accuracy is: 1.0\n" ] }, { @@ -184,11 +233,84 @@ "text": [ "\n" ] - }, + } + ], + "source": [ + "x_clean = HuggingFaceMultiModalInput(**inputs)\n", + "clean_outputs = classifier.predict(x_clean)\n", + "clean_preds = np.argmax(clean_outputs, axis=1)\n", + "\n", + "clean_acc = np.mean(clean_preds == labels.cpu().detach().numpy())\n", + "print('The clean accuracy is:', clean_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4c8e2a1c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clean_images = np.clip(np.transpose(x_clean['pixel_values'], (0, 2, 3, 1)) * STD + MEAN, 0, 1)\n", + "\n", + "fig, ax = plt.subplots(1, 4, figsize=(9, 4))\n", + "fig.tight_layout()\n", + "for i in range(4):\n", + " ax[i].imshow(clean_images[i])\n", + " ax[i].axis('off')\n", + " ax[i].set_title(text[clean_preds[i]])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "89365854", + "metadata": {}, + "source": [ + "## Evasion Attack\n", + "\n", + "We will perform a Projected Gradient Descent (PGD) evasion attack against the CLIP model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a7ea15ca", + "metadata": {}, + "outputs": [], + "source": [ + "norm_bound_eps = np.asarray([8 / 255, 8 / 255, 8 / 255]) / STD\n", + "\n", + "attack = CLIPProjectedGradientDescentNumpy(\n", + " classifier,\n", + " max_iter=10,\n", + " eps=np.ones((3, 224, 224)) * np.reshape([norm_bound_eps], (3, 1, 1)),\n", + " eps_step=np.ones((3, 224, 224)) * 0.1,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "31ab7e59", + "metadata": {}, + "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04de95b8f8e244eea99333f888cdd862", + "model_id": "13602a2c1e5b4c5aac427b12c41568e0", "version_major": 2, "version_minor": 0 }, @@ -203,29 +325,116 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2.15it/s]\n" + "100%|██████████| 1/1 [00:00<00:00, 10.37it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The adversarial accuracy is 0.0\n" + "The adversarial accuracy is: 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] } ], "source": [ - "# Running the attack we see the performance drop from 100% to 0%.\n", - "attack(input_list, text, labels)" + "x_adv = attack.generate(x_clean, labels)\n", + "adv_outputs = classifier.predict(x_adv)\n", + "adv_preds = np.argmax(adv_outputs, axis=1)\n", + "\n", + "adv_acc = np.mean(adv_preds == labels.cpu().detach().numpy())\n", + "print('The adversarial accuracy is:', adv_acc)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "031a35b7", + "execution_count": 14, + "id": "9a03d2f4", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "adv_images = np.clip(np.transpose(x_adv['pixel_values'], (0, 2, 3, 1)) * STD + MEAN, 0, 1)\n", + "\n", + "fig, ax = plt.subplots(1, 4, figsize=(9, 4))\n", + "fig.tight_layout()\n", + "for i in range(4):\n", + " ax[i].imshow(adv_images[i])\n", + " ax[i].axis('off')\n", + " ax[i].set_title(text[adv_preds[i]])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "52ae133d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "delta = (clean_images - adv_images + 8/255) * 10\n", + "\n", + "fig, axs = plt.subplots(4, 3, figsize=(8, 10))\n", + "fig.tight_layout()\n", + "for i in range(4):\n", + " axs[i, 0].imshow(clean_images[i])\n", + " axs[i, 0].set_xlabel(text[clean_preds[i]])\n", + " axs[i, 0].tick_params(axis='both', which='both',length=0)\n", + " axs[i, 0].axes.xaxis.set_ticklabels([])\n", + " axs[i, 0].axes.yaxis.set_ticklabels([])\n", + " axs[i, 1].imshow(adv_images[i])\n", + " axs[i, 1].axes.xaxis.set_ticklabels([])\n", + " axs[i, 1].axes.yaxis.set_ticklabels([])\n", + " axs[i, 1].tick_params(axis='both', which='both',length=0)\n", + " axs[i, 1].set_xlabel(text[adv_preds[i]])\n", + " im = axs[i, 2].imshow(delta[i])\n", + " axs[i, 2].axis('off')\n", + " fig.colorbar(im)\n", + "\n", + "axs[0, 0].set_title('clean')\n", + "axs[0, 1].set_title('adversarial')\n", + "axs[0, 2].set_title('delta')\n", + "plt.tight_layout()\n", + "plt.show()" + ] } ], "metadata": { @@ -244,7 +453,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.6" } }, "nbformat": 4,