adversarial_training_cifar10.py

# -*- coding: utf-8 -*-
"""
Trains a convolutional neural network on the CIFAR-10 dataset, then generated adversarial images using the
DeepFool attack and retrains the network on the training set augmented with the adversarial images.
"""
# noqa: E402
from __future__ import absolute_import, division, print_function, unicode_literals

import logging

import tensorflow as tf

tf.compat.v1.disable_eager_execution()

from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout
import numpy as np

from art.attacks.evasion import DeepFool
from art.estimators.classification import KerasClassifier
from art.utils import load_dataset

# Configure a logger to capture ART outputs; these are printed in console and the level of detail is set to INFO
logger = logging.getLogger()
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(levelname)s] %(message)s")
handler.setFormatter(formatter)
logger.addHandler(handler)

# Read CIFAR10 dataset
(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset(str("cifar10"))
x_train, y_train = x_train[:5000], y_train[:5000]
x_test, y_test = x_test[:500], y_test[:500]
im_shape = x_train[0].shape

# Create Keras convolutional neural network - basic architecture from Keras examples
# Source here: https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py
model = Sequential()
model.add(Conv2D(32, (3, 3), padding="same", input_shape=x_train.shape[1:]))
model.add(Activation("relu"))
model.add(Conv2D(32, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation("softmax"))

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# Create classifier wrapper
classifier = KerasClassifier(model=model, clip_values=(min_, max_))
classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)

# Craft adversarial samples with DeepFool
logger.info("Create DeepFool attack")
adv_crafter = DeepFool(classifier)
logger.info("Craft attack on training examples")
x_train_adv = adv_crafter.generate(x_train)
logger.info("Craft attack test examples")
x_test_adv = adv_crafter.generate(x_test)

# Evaluate the classifier on the adversarial samples
preds = np.argmax(classifier.predict(x_test_adv), axis=1)
acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info("Classifier before adversarial training")
logger.info("Accuracy on adversarial samples: %.2f%%", (acc * 100))

# Data augmentation: expand the training set with the adversarial samples
x_train = np.append(x_train, x_train_adv, axis=0)
y_train = np.append(y_train, y_train, axis=0)

# Retrain the CNN on the extended dataset
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)

# Evaluate the adversarially trained classifier on the test set
preds = np.argmax(classifier.predict(x_test_adv), axis=1)
acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info("Classifier with adversarial training")
logger.info("Accuracy on adversarial samples: %.2f%%", (acc * 100))