x_train.shape[1:], 300, 100, dropout, 0, 0)
elif args.experiment_type == "five_layer_dnn":
kmodel = neural_networks.symmetric_five_layer_nn_foolbox(
x_train.shape[1:], dropout, dropout)
elif args.experiment_type == "six_layer_dnn":
kmodel = neural_networks.asymmetric_six_layer_nn_foolbox(
x_train.shape[1:], dropout, dropout)
# elif args.experiment_type == "VGG":
# classifier = convolutional.mini_VGG(dropout_levels[dropout], "mnist")
# elif args.experiment_type == "leNet5":
# classifier = convolutional.leNet_cnn_single(dropout_levels[dropout])
kmodel.fit(x_train, y_train, epochs=50, batch_size=128)
# kmodel.fit(x_train, y_train, epochs=20, batch_size=128)
attack = RandomPGD(model=kmodel)
adversarial = attack(x_test[:10],
np.argmax(y_test[:10], axis=1),
iterations=60)
# adversarial = attack(x_test[:1000], np.argmax(y_test[:1000], axis=1), iterations=60)
# For those samples for which the L2 method does not produce an adversarial sample within the attack parameters,
# we exclude them from the perturbation evaluation. the None given by the attack with the original input.
failed = 0
perturbations = []
adv_examples = []
orig_examples = []
correct_labels = []
model = convolutional.vgg_model_wide(args.dataset, 0, l1, 0)
elif args.experiment_type == "leNet":
model = convolutional.leNet_model_wide(0, l1, 0)
else:
raise Exception("Invalid model!")
model.fit(x_train, y_train, epochs=50, batch_size=128)
preds = np.argmax(model.predict(x_test), axis=1)
kmodel = KerasModel(model=model, bounds=(min_, max_))
attack = None
if args.attack_type == 'l2':
attack = CarliniWagnerL2Attack(kmodel, TargetClass(7))
elif args.attack_type == 'linf':
attack = RandomPGD(kmodel, TargetClass(7))
x_sample = np.take(x_test, ones, axis=0)
# We exclude by default those examples which are not predicted by the classifier as 1s.
true_ones = np.where(preds == 1)[0]
x_sample = np.take(x_sample, true_ones, axis=0)
y_sample = np.array([to_one_hot(1) for _ in x_sample])
adversarial = None
if args.attack_type == 'l2':
adversarial = attack(x_sample,
np.argmax(y_sample, axis=1),
binary_search_steps=5,
max_iterations=600)
model = convolutional.leNet_dense(dropout, 0, 0)
else:
raise Exception("Invalid dropout style!")
else:
raise Exception("Invalid model!")
model.fit(x_train, y_train, epochs=50, batch_size=128)
preds = np.argmax(model.predict(x_test), axis=1)
kmodel = KerasModel(model=model, bounds=(min_, max_))
attack = None
if args.attack_type == 'l2':
attack = CarliniWagnerL2Attack(kmodel, Misclassification())
elif args.attack_type == 'linf':
attack = RandomPGD(kmodel, Misclassification())
x_sample = x_test[:1000]
y_sample = y_test[:1000]
adversarial = None
if args.attack_type == 'l2':
adversarial = attack(x_sample, np.argmax(y_sample, axis=1), binary_search_steps=5, max_iterations=600)
else:
adversarial = attack(x_sample, np.argmax(y_sample, axis=1), iterations=30)
failed = 0
misclassified = 0
perturbations = []
adv_examples = []