def test_8_keras_iris_vector_unbounded(self):
classifier = get_tabular_classifier_kr()
# Recreate a classifier without clip values
classifier = KerasClassifier(model=classifier._model,
use_logits=False,
channels_first=True)
attack = SaliencyMapMethod(classifier, theta=1, verbose=False)
x_test_iris_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
def _jsma(model, data, labels, attack_args):
theta = attack_args.get('theta', 0.15)
gamma = attack_args.get('gamma', 0.5)
batch_size = attack_args.get('batch_size', 1)
attacker = SaliencyMapMethod(classifier=model,
theta=theta,
gamma=gamma,
batch_size=batch_size)
return attacker.generate(data, labels)
def test_check_params(self):
ptc = get_image_classifier_pt(from_logits=True)
with self.assertRaises(ValueError):
_ = SaliencyMapMethod(ptc, gamma=-1)
with self.assertRaises(ValueError):
_ = SaliencyMapMethod(ptc, batch_size=-1)
with self.assertRaises(ValueError):
_ = SaliencyMapMethod(ptc, verbose="False")
def test_4_pytorch_iris_vector(self):
classifier = get_tabular_classifier_pt()
attack = SaliencyMapMethod(classifier, theta=1, verbose=False)
x_test_iris_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
self.assertTrue((x_test_iris_adv <= 1).all())
self.assertTrue((x_test_iris_adv >= 0).all())
preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_iris,
axis=1) == preds_adv).all())
accuracy = np.sum(preds_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("Accuracy on Iris with JSMA adversarial examples: %.2f%%",
(accuracy * 100))
def test_6_scikitlearn(self):
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from art.estimators.classification.scikitlearn import SklearnClassifier
scikitlearn_test_cases = [
LogisticRegression(solver="lbfgs", multi_class="auto"),
SVC(gamma="auto"),
LinearSVC(),
]
x_test_original = self.x_test_iris.copy()
for model in scikitlearn_test_cases:
classifier = SklearnClassifier(model=model, clip_values=(0, 1))
classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
attack = SaliencyMapMethod(classifier,
theta=1,
batch_size=128,
verbose=False)
x_test_iris_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
self.assertTrue((x_test_iris_adv <= 1).all())
self.assertTrue((x_test_iris_adv >= 0).all())
preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_iris,
axis=1) == preds_adv).all())
accuracy = np.sum(preds_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info(
"Accuracy of " + classifier.__class__.__name__ +
" on Iris with JSMA adversarial examples: "
"%.2f%%",
(accuracy * 100),
)
# Check that x_test has not been modified by attack and classifier
self.assertAlmostEqual(float(
np.max(np.abs(x_test_original - self.x_test_iris))),
0.0,
delta=0.00001)
def get_adversarial_examples(X, Y, model, nb_classes, attack=None):
assert model is not None
assert attack is not None
art_classifier = SklearnClassifier(model=model,
clip_values=(0, nb_classes))
attacker = None
if attack == ATTACK.PGD:
attacker = ProjectedGradientDescent(classifier=art_classifier,
norm=np.inf,
eps=0.2,
eps_step=0.1,
max_iter=3,
targeted=False,
num_random_init=0,
batch_size=128)
elif attack == ATTACK.DEEPFOOL:
attacker = DeepFool(classifier=art_classifier,
max_iter=5,
epsilon=1e-6,
nb_grads=3,
batch_size=1)
elif attack == ATTACK.FGSM:
attacker = FastGradientMethod(classifier=art_classifier,
norm=np.inf,
eps=0.3,
targeted=False,
batch_size=128)
elif attack == ATTACK.BIM:
attacker = BasicIterativeMethod(classifier=art_classifier,
eps=0.3,
eps_step=0.1,
targeted=False,
batch_size=128)
elif attack == ATTACK.JSMA:
attacker = SaliencyMapMethod(classifier=art_classifier,
theta=0.3,
gamma=0.5,
batch_size=128)
elif attack == ATTACK.CW_L2:
attacker = CarliniL2Method(classifier=art_classifier,
learning_rate=0.1)
elif attack == ATTACK.CW_Linf:
attacker = CarliniLInfMethod(classifier=art_classifier,
learning_rate=0.01)
else:
raise NotImplementedError(attack, 'is not implemented.')
print(
'Generating [{}] adversarial examples, it will take a while...'.format(
attack))
X_adv = attacker.generate(X, y=Y)
del attacker
return X_adv
def test_9_keras_mnist(self):
x_test_original = self.x_test_mnist.copy()
# Keras classifier
classifier = get_image_classifier_kr()
scores = classifier._model.evaluate(self.x_train_mnist,
self.y_train_mnist)
logger.info("[Keras, MNIST] Accuracy on training set: %.2f%%",
(scores[1] * 100))
scores = classifier._model.evaluate(self.x_test_mnist,
self.y_test_mnist)
logger.info("[Keras, MNIST] Accuracy on test set: %.2f%%",
(scores[1] * 100))
# targeted
# Generate random target classes
nb_classes = np.unique(np.argmax(self.y_test_mnist, axis=1)).shape[0]
targets = np.random.randint(nb_classes, size=self.n_test)
while (targets == np.argmax(self.y_test_mnist, axis=1)).any():
targets = np.random.randint(nb_classes, size=self.n_test)
# Perform attack
df = SaliencyMapMethod(classifier,
theta=1,
batch_size=100,
verbose=False)
x_test_adv = df.generate(self.x_test_mnist,
y=to_categorical(targets, nb_classes))
self.assertFalse((self.x_test_mnist == x_test_adv).all())
self.assertFalse((0.0 == x_test_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_adv))
self.assertFalse((self.y_test_mnist == y_pred).all())
accuracy = np.sum(
np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist,
axis=1)) / self.n_test
logger.info("Accuracy on adversarial examples: %.2f%%",
(accuracy * 100))
# untargeted
df = SaliencyMapMethod(classifier,
theta=1,
batch_size=100,
verbose=False)
x_test_adv = df.generate(self.x_test_mnist)
self.assertFalse((self.x_test_mnist == x_test_adv).all())
self.assertFalse((0.0 == x_test_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_adv))
self.assertFalse((self.y_test_mnist == y_pred).all())
accuracy = np.sum(
np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist,
axis=1)) / self.n_test
logger.info("Accuracy on adversarial examples: %.2f%%",
(accuracy * 100))
# Check that x_test has not been modified by attack and classifier
self.assertAlmostEqual(float(
np.max(np.abs(x_test_original - self.x_test_mnist))),
0.0,
delta=0.00001)
def craft(X, Y, art_classifier, attack=None, **attack_params):
assert art_classifier is not None
assert attack is not None
attacker = None
if attack == ATTACK.PGD:
eps = attack_params.get('eps', 0.2)
eps_step = attack_params.get('eps_step', eps / 5.)
max_iter = attack_params.get('max_iter', 3)
targeted = attack_params.get('targeted', False)
batch_size = attack_params.get('batch_size', 128)
attacker = ProjectedGradientDescent(classifier=art_classifier,
norm=np.inf,
eps=eps,
eps_step=eps_step,
max_iter=max_iter,
targeted=targeted,
num_random_init=0,
batch_size=batch_size)
elif attack == ATTACK.DEEPFOOL:
eps = attack_params.get('eps', 1e-6)
max_iter = attack_params.get('max_iter', 5)
nb_grads = attack_params.get('nb_grads', 3)
batch_size = attack_params.get('batch_size', 1)
attacker = DeepFool(classifier=art_classifier,
max_iter=max_iter,
epsilon=eps,
nb_grads=nb_grads,
batch_size=batch_size)
elif attack == ATTACK.FGSM:
eps = attack_params.get('eps', 0.3)
targeted = attack_params.get('targeted', False)
batch_size = attack_params.get('batch_size', 128)
attacker = FastGradientMethod(classifier=art_classifier,
norm=np.inf,
eps=eps,
targeted=targeted,
batch_size=batch_size)
elif attack == ATTACK.BIM:
eps = attack_params.get('eps', 0.3)
eps_step = attack_params.get('eps_step', eps / 5.)
norm = attack_params.get('norm', np.inf)
targeted = attack_params.get('targeted', False)
batch_size = attack_params.get('batch_size', 128)
attacker = BasicIterativeMethod(classifier=art_classifier,
norm=norm,
eps=eps,
eps_step=eps_step,
targeted=targeted,
batch_size=batch_size)
elif attack == ATTACK.JSMA:
theta = attack_params.get('theta', 0.3)
gamma = attack_params.get('gamma', 0.5)
batch_size = attack_params.get('batch_size', 128)
attacker = SaliencyMapMethod(classifier=art_classifier,
theta=theta,
gamma=gamma,
batch_size=batch_size)
elif attack == ATTACK.CW_L2:
lr = attack_params.get('lr', 0.1)
bsearch_steps = attack_params.get('bsearch_steps', 10)
attacker = CarliniL2Method(classifier=art_classifier,
learning_rate=lr,
binary_search_steps=bsearch_steps)
elif attack == ATTACK.CW_Linf:
lr = attack_params.get('lr', 0.01)
attacker = CarliniLInfMethod(classifier=art_classifier,
learning_rate=lr)
else:
raise NotImplementedError(attack, 'is not implemented.')
print(
'Generating [{}] adversarial examples, it will take a while...'.format(
attack))
X_adv = attacker.generate(X, y=Y)
del attacker
return X_adv
def test_pytorch_mnist(self):
x_train_mnist = np.swapaxes(self.x_train_mnist, 1,
3).astype(np.float32)
x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
x_test_original = x_test_mnist.copy()
# Create basic PyTorch model
classifier = get_image_classifier_pt()
scores = get_labels_np_array(classifier.predict(x_train_mnist))
accuracy = np.sum(
np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist,
axis=1)) / self.n_train
logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%",
(accuracy * 100))
scores = get_labels_np_array(classifier.predict(x_test_mnist))
accuracy = np.sum(
np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist,
axis=1)) / self.n_test
logger.info("\n[PyTorch, MNIST] Accuracy on test set: %.2f%%",
(accuracy * 100))
# targeted
# Generate random target classes
nb_classes = np.unique(np.argmax(self.y_test_mnist, axis=1)).shape[0]
targets = np.random.randint(nb_classes, size=self.n_test)
while (targets == np.argmax(self.y_test_mnist, axis=1)).any():
targets = np.random.randint(nb_classes, size=self.n_test)
# Perform attack
df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
x_test_mnist_adv = df.generate(x_test_mnist,
y=to_categorical(targets, nb_classes))
self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
self.assertFalse((0.0 == x_test_mnist_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_mnist_adv))
self.assertFalse((self.y_test_mnist == y_pred).all())
accuracy = np.sum(
np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist,
axis=1)) / self.n_test
logger.info("Accuracy on adversarial examples: %.2f%%",
(accuracy * 100))
# untargeted
df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
x_test_mnist_adv = df.generate(x_test_mnist)
self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
self.assertFalse((0.0 == x_test_mnist_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_mnist_adv))
self.assertFalse((self.y_test_mnist == y_pred).all())
accuracy = np.sum(
np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist,
axis=1)) / self.n_test
logger.info("Accuracy on adversarial examples: %.2f%%",
(accuracy * 100))
# Check that x_test has not been modified by attack and classifier
self.assertAlmostEqual(float(
np.max(np.abs(x_test_original - x_test_mnist))),
0.0,
delta=0.00001)