criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
# Step 3: Create the ART classifier
classifier = PyTorchClassifier(
model=model,
clip_values=(0, 1),
loss=criterion,
optimizer=optimizer,
input_shape=(1, 28, 28),
nb_classes=10,
)
classifier.fit(x_train, y_train, batch_size=128, nb_epochs=5)
predictions = classifier.predict(x_test)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print("Accuracy on benign test examples: {}%".format(accuracy * 100))
def calculate_l0(batch_original, batch_adversarial, dim):
# image_original==x_test_adv
matrix_bool = batch_original == batch_adversarial
inverse_matrix = np.logical_not(matrix_bool)
l0 = np.count_nonzero(inverse_matrix, axis=dim)
return l0
optimizer = optim.Adam(model.parameters(), lr=0.01)
# Step 3: Create the ART classifier
classifier = PyTorchClassifier(
model=model,
clip_values=(0.0, 1.0),
preprocessing=(cifar_mu, cifar_std),
loss=criterion,
optimizer=optimizer,
input_shape=(3, 32, 32),
nb_classes=10,
)
# Step 4: Train the ART classifier
classifier.fit(x_train, y_train, batch_size=64, nb_epochs=10)
exp_time = time.strftime('%H_%M_%S')
# torch.save(classifier.model.state_dict(), 'pth/{}.pth.tar'.format(exp_time))
# Step 5: Evaluate the ART classifier on benign test examples
predictions = classifier.predict(x_test)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print("Accuracy on benign test examples: {}%".format(accuracy * 100))
# Step 6: Generate adversarial test examples
attack = FastGradientMethod(estimator=classifier, eps=0.2)
attack_pgd = ProjectedGradientDescent(
classifier,
norm=np.inf,
