def _test_backend_mnist(self, classifier, x_test, y_test):
x_test_original = x_test.copy()
df = VirtualAdversarialMethod(classifier, batch_size=100)
from art.classifiers import TensorFlowClassifier
if isinstance(classifier, TensorFlowClassifier):
with self.assertRaises(TypeError) as context:
x_test_adv = df.generate(x_test)
self.assertIn('This attack requires a classifier predicting probabilities in the range [0, 1] as output.'
'Values smaller than 0.0 or larger than 1.0 have been detected.', str(context.exception))
else:
x_test_adv = df.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_adv))
self.assertFalse((y_test == y_pred).all())
acc = np.sum(np.argmax(y_pred, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info('Accuracy on adversarial examples: %.2f%%', (acc * 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))), 0.0, delta=0.00001)
def _test_backend_mnist(self, classifier):
# Get MNIST
(_, _), (x_test, y_test) = self.mnist
x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
df = VirtualAdversarialMethod(classifier, batch_size=100)
from art.classifiers import TensorFlowClassifier
if isinstance(classifier, TensorFlowClassifier):
with self.assertRaises(TypeError) as context:
x_test_adv = df.generate(x_test)
self.assertIn(
'This attack requires a classifier predicting probabilities in the range [0, 1] as output.'
'Values smaller than 0.0 or larger than 1.0 have been detected.',
str(context.exception))
else:
x_test_adv = df.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_adv))
self.assertFalse((y_test == y_pred).all())
acc = np.sum(
np.argmax(y_pred, axis=1) == np.argmax(
y_test, axis=1)) / y_test.shape[0]
logging.info('Accuracy on adversarial examples: %.2f%%',
(acc * 100))
def test_pytorch_iris(self):
(_, _), (x_test, y_test) = self.iris
classifier = get_iris_classifier_pt()
attack = VirtualAdversarialMethod(classifier, eps=.1)
with self.assertRaises(TypeError) as context:
x_test_adv = attack.generate(x_test.astype(np.float32))
self.assertIn('This attack requires a classifier predicting probabilities in the range [0, 1] as output.'
'Values smaller than 0.0 or larger than 1.0 have been detected.', str(context.exception))
def test_tensorflow_iris(self):
classifier, _ = get_tabular_classifier_tf()
attack = VirtualAdversarialMethod(classifier, eps=0.1)
with self.assertRaises(TypeError) as context:
x_test_iris_adv = attack.generate(self.x_test_iris)
self.assertIn(
"This attack requires a classifier predicting probabilities in the range [0, 1] as output."
"Values smaller than 0.0 or larger than 1.0 have been detected.",
str(context.exception),
)
def test_keras_iris_clipped(self):
classifier = get_tabular_classifier_kr()
# Test untargeted attack
attack = VirtualAdversarialMethod(classifier, eps=0.1)
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())
acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("Accuracy on Iris with VAT adversarial examples: %.2f%%", (acc * 100))
def test_keras_iris_unbounded(self):
classifier = get_tabular_classifier_kr()
# Recreate a classifier without clip values
classifier = KerasClassifier(model=classifier._model, use_logits=False, channel_index=1)
attack = VirtualAdversarialMethod(classifier, eps=1)
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).any())
self.assertTrue((x_test_iris_adv < 0).any())
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())
acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("Accuracy on Iris with VAT adversarial examples: %.2f%%", (acc * 100))
def test_keras_iris_clipped(self):
(_, _), (x_test, y_test) = self.iris
classifier = get_iris_classifier_kr()
# Test untargeted attack
attack = VirtualAdversarialMethod(classifier, eps=.1)
x_test_adv = attack.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1).all())
self.assertTrue((x_test_adv >= 0).all())
preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info('Accuracy on Iris with VAT adversarial examples: %.2f%%', (acc * 100))
def _test_backend_mnist(self, classifier, x_test, y_test):
x_test_original = x_test.copy()
df = VirtualAdversarialMethod(classifier, batch_size=100, max_iter=2)
x_test_adv = df.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
y_pred = get_labels_np_array(classifier.predict(x_test_adv))
self.assertFalse((y_test == y_pred).all())
acc = np.sum(np.argmax(y_pred, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info("Accuracy on adversarial examples: %.2f%%", (acc * 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))), 0.0, delta=0.00001)
def GetAttackers(classifier, x_test, attacker_name):
"""
Function:
Load classifier and generate adversarial samples
"""
t_start = time.time()
if attacker_name == "FGSM":
attacker = FastGradientMethod(classifier=classifier, eps=0.3)
elif attacker_name == "Elastic":
attacker = ElasticNet(classifier=classifier, confidence=0.5)
elif attacker_name == "BasicIterativeMethod":
attacker = BasicIterativeMethod(classifier=classifier, eps=0.3)
elif attacker_name == "NewtonFool":
attacker = NewtonFool(classifier=classifier, max_iter=20)
elif attacker_name == "HopSkipJump":
attacker = HopSkipJump(classifier=classifier, max_iter=20)
elif attacker_name == "ZooAttack":
attacker = ZooAttack(classifier=classifier, max_iter=20)
elif attacker_name == "VirtualAdversarialMethod":
attacker = VirtualAdversarialMethod(classifier=classifier, max_iter=20)
elif attacker_name == "UniversalPerturbation":
attacker = UniversalPerturbation(classifier=classifier, max_iter=20)
elif attacker_name == "AdversarialPatch":
attacker = AdversarialPatch(classifier=classifier, max_iter=20)
elif attacker_name == "Attack":
attacker = Attack(classifier=classifier)
elif attacker_name == "BoundaryAttack":
attacker = BoundaryAttack(classifier=classifier,
targeted=False,
epsilon=0.05,
max_iter=20) #, max_iter=20
elif attacker_name == "CarliniL2":
attacker = CarliniL2Method(classifier=classifier,
confidence=0.5,
learning_rate=0.001,
max_iter=15)
elif attacker_name == "CarliniLinf":
attacker = CarliniLInfMethod(classifier=classifier,
confidence=0.5,
learning_rate=0.001,
max_iter=15)
elif attacker_name == "DeepFool":
attacker = DeepFool(classifier)
elif attacker_name == "SMM":
attacker = SaliencyMapMethod(classifier=classifier, theta=2)
elif attacker_name == "PGD":
attacker = ProjectedGradientDescent(classifier=classifier,
norm=2,
eps=1,
eps_step=0.5)
else:
raise ValueError("Please get the right attacker's name for the input.")
test_adv = attacker.generate(x_test)
dt = time.time() - t_start
return test_adv, dt
def test_classifier_type_check_fail_classifier(self):
# Use a useless test classifier to test basic classifier properties
class ClassifierNoAPI:
pass
classifier = ClassifierNoAPI
with self.assertRaises(TypeError) as context:
_ = VirtualAdversarialMethod(classifier=classifier)
self.assertIn('For `VirtualAdversarialMethod` classifier must be an instance of '
'`art.classifiers.classifier.Classifier`, the provided classifier is instance of '
'(<class \'object\'>,).', str(context.exception))
def test_classifier_type_check_fail_gradients(self):
# Use a test classifier not providing gradients required by white-box attack
from art.classifiers.scikitlearn import ScikitlearnDecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
classifier = ScikitlearnDecisionTreeClassifier(model=DecisionTreeClassifier())
with self.assertRaises(TypeError) as context:
_ = VirtualAdversarialMethod(classifier=classifier)
self.assertIn('For `VirtualAdversarialMethod` classifier must be an instance of '
'`art.classifiers.classifier.ClassifierNeuralNetwork` and '
'`art.classifiers.classifier.ClassifierGradients`, the provided classifier is instance of '
'(<class \'art.classifiers.scikitlearn.ScikitlearnClassifier\'>,).', str(context.exception))
