def test_failure_attack(self):
"""
Test the corner case when attack is failed.
:return:
"""
# Build TFClassifier
tfc, sess = get_classifier_tf()
# Get MNIST
(_, _), (x_test, y_test) = self.mnist
# Failure attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=True,
max_iter=0,
learning_rate=0,
eps=0.5)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
self.assertTrue(np.allclose(x_test, x_test_adv, atol=1e-3))
# Clean-up session
sess.close()
tf.reset_default_graph()
def carlini_inf(x_test, model, eps, max_iter, learning_rate):
classifier = KerasClassifier(model=model, clip_values=(0, 1))
attack_cw = CarliniLInfMethod(classifier=classifier,
eps=eps,
max_iter=max_iter,
learning_rate=learning_rate)
x_test_adv = attack_cw.generate(x_test)
return np.reshape(x_test_adv, (32, 32, 3))
def test_pytorch_iris_LInf(self):
classifier = get_tabular_classifier_pt()
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
x_test_adv = attack.generate(self.x_test_iris.astype(np.float32))
self.assertFalse((self.x_test_iris == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
def test_keras_iris_unbounded_LInf(self):
classifier = get_tabular_classifier_kr()
# Recreate a classifier without clip values
classifier = KerasClassifier(model=classifier._model, use_logits=False, channel_index=1)
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=1)
x_test_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_adv).all())
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
def test_iris_pt(self):
(_, _), (x_test, y_test) = self.iris
classifier = get_iris_classifier_pt()
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
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 C&W adversarial examples: %.2f%%', (acc * 100))
def test_iris_k_unbounded(self):
(_, _), (x_test, y_test) = self.iris
classifier, _ = get_iris_classifier_kr()
# Recreate a classifier without clip values
classifier = KerasClassifier(model=classifier._model, use_logits=False, channel_index=1)
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=1)
x_test_adv = attack.generate(x_test)
self.assertFalse((x_test == x_test_adv).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 C&W adversarial examples: %.2f%%', (acc * 100))
def evaluate_cw(self, data_loader):
eps = attack_configs['PGD'][self.dataset]['epsilon']
adv_crafter = CarliniLInfMethod(self.classifier,
targeted=False,
eps=eps)
data_iter = iter(data_loader)
examples, labels = next(data_iter)
examples, labels = examples.cpu().numpy(), labels.cpu().numpy()
labels_one_hot = np.eye(self.nb_classes)[labels]
examples_adv = adv_crafter.generate(examples, y=labels_one_hot)
preds = np.argmax(self.classifier.predict(examples_adv), axis=1)
acc = np.sum(preds == labels) / labels.shape[0]
return acc
def test_failure_attack(self):
"""
Test the corner case when attack is failed.
:return:
"""
# Build a TFClassifier
# Define input and output placeholders
input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
logits = tf.layers.dense(fc, 10)
# Train operator
loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train = optimizer.minimize(loss)
# Tensorflow session and initialization
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Get MNIST
(x_train, y_train), (x_test, y_test) = self.mnist
# Train the classifier
tfc = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss,
None, sess)
tfc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=10)
# Failure attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=True,
max_iter=0,
learning_rate=0,
eps=0.5)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
np.testing.assert_almost_equal(x_test, x_test_adv, 3)
def general_test(model,
optimizer,
input_shape,
nb_classes,
test_loader,
method,
btrain=False,
model_file='last_model_92_sgd.pkl'):
global _classes
if not btrain:
model.load_state_dict(torch.load(model_file))
model.eval()
loss = nn.CrossEntropyLoss()
warped_model = PyTorchClassifier(model,
loss,
optimizer,
input_shape,
nb_classes,
clip_values=(.0, 1.))
if method == 'Deepfool':
adv_crafter = DeepFool(warped_model)
elif method == 'BIM':
adv_crafter = BasicIterativeMethod(warped_model, batch_size=20)
elif method == 'JSMA':
adv_crafter = SaliencyMapMethod(warped_model, batch_size=20)
elif method == 'CW2':
adv_crafter = CarliniL2Method(warped_model, batch_size=20)
elif method == 'CWI':
adv_crafter = CarliniLInfMethod(warped_model, batch_size=20)
correct, total = 0, 0
class_correct = list(0. for _ in range(10))
class_total = list(0. for _ in range(10))
for images, labels in test_loader:
images = adv_crafter.generate(images.numpy())
images = Variable(torch.from_numpy(images).cuda())
labels = Variable(labels.cuda())
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels.data).sum()
c = (predicted == labels.data).squeeze()
for i in range(20):
label = labels.data[i]
class_correct[label] += c[i]
class_total[label] += 1
print('Accuracy of the model on the test images: %d %%' %
(100 * float(correct) / total))
print('Accuracy of the model on the test images:', float(correct) / total)
for i in range(10):
print('Accuracy of %5s : %2d %%' %
(_classes[i], 100 * class_correct[i] / class_total[i]))
return correct / total
def test_keras_iris_clipped_LInf(self):
(_, _), (x_test, y_test) = self.iris
classifier = get_iris_classifier_kr()
attack = CarliniLInfMethod(classifier,
targeted=False,
max_iter=10,
eps=0.5)
x_test_adv = attack.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(y_test, axis=1) == predictions_adv).all())
accuracy = np.sum(
predictions_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info('Accuracy on Iris with C&W adversarial examples: %.2f%%',
(accuracy * 100))
def general_test_v2(model,
optimizer,
input_shape,
nb_classes,
test_loader,
method,
conf,
btrain=False,
model_file='last_model_92_sgd.pkl'):
global _classes
if not btrain:
checked_state = torch.load(model_file)['state_dict']
model.load_state_dict(checked_state)
model.eval()
loss = nn.CrossEntropyLoss()
warped_model = PyTorchClassifier(model,
loss,
optimizer,
input_shape,
nb_classes,
clip_values=(.0, 1.))
if method == 'Deepfool':
adv_crafter = DeepFool(warped_model)
elif method == 'BIM':
adv_crafter = BasicIterativeMethod(warped_model, batch_size=32)
elif method == 'JSMA':
adv_crafter = SaliencyMapMethod(warped_model, batch_size=32)
elif method == 'CW2':
adv_crafter = CarliniL2Method(warped_model, batch_size=32)
elif method == 'CWI':
adv_crafter = CarliniLInfMethod(warped_model, batch_size=32)
elif method == 'FGSM':
adv_crafter = FastGradientMethod(warped_model, batch_size=32)
correct, total = 0, 0
adv_dataset = adv_generalization(test_loader, adv_crafter, conf)
temp_loader = DataLoader(dataset=adv_dataset,
batch_size=32,
shuffle=False,
drop_last=True)
# temp_loader = test_loader
for images, labels in temp_loader:
images = Variable(images.cuda())
labels = Variable(labels.cuda())
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels.data).sum()
print('Accuracy of the model on the test images: %d %%' %
(100 * float(correct) / total))
print('Accuracy of the model on the test images:', float(correct) / total)
return correct / total
def test_tensorflow_failure_attack_LInf(self):
"""
Test the corner case when attack is failed.
:return:
"""
# Build TensorFlowClassifier
tfc, sess = get_image_classifier_tf(from_logits=True)
# Failure attack
clinfm = CarliniLInfMethod(classifier=tfc, targeted=True, max_iter=0, learning_rate=0, eps=0.5)
params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes())}
x_test_adv = clinfm.generate(self.x_test_mnist, **params)
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
self.assertTrue(np.allclose(self.x_test_mnist, x_test_adv, atol=1e-3))
# Clean-up session
if sess is not None:
sess.close()
def test_pytorch_mnist_LInf(self):
"""
Third test with the PyTorchClassifier.
:return:
"""
x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
# Build PyTorchClassifier
ptc = get_image_classifier_pt(from_logits=True)
# First attack
clinfm = CarliniLInfMethod(classifier=ptc, targeted=True, max_iter=10, eps=0.5)
params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes())}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0 + 1e-6)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params["y"], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=ptc, targeted=False, max_iter=10, eps=0.5)
x_test_adv = clinfm.generate(x_test)
self.assertLessEqual(np.amax(x_test_adv), 1.0 + 1e-6)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params["y"], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target != y_pred_adv).any())
def test_tensorflow_iris_LInf(self):
classifier, _ = get_tabular_classifier_tf()
# Test untargeted attack
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
x_test_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
# Test targeted attack
targets = random_targets(self.y_test_iris, nb_classes=3)
attack = CarliniLInfMethod(classifier, targeted=True, max_iter=10, eps=0.5)
x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
self.assertFalse((self.x_test_iris == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
logger.info("Success rate of targeted C&W on Iris: %.2f%%", (accuracy * 100))
def test_iris_tf(self):
(_, _), (x_test, y_test) = self.iris
classifier, _ = get_iris_classifier_tf()
# Test untargeted attack
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
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 C&W adversarial examples: %.2f%%', (acc * 100))
# Test targeted attack
targets = random_targets(y_test, nb_classes=3)
attack = CarliniLInfMethod(classifier, targeted=True, max_iter=10, eps=0.5)
x_test_adv = attack.generate(x_test, **{'y': targets})
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.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / y_test.shape[0]
logger.info('Success rate of targeted C&W on Iris: %.2f%%', (acc * 100))
def test_ptclassifier(self):
"""
Third test with the PyTorchClassifier.
:return:
"""
# Build PyTorchClassifier
ptc = get_classifier_pt()
# Get MNIST
(_, _), (x_test, y_test) = self.mnist
x_test = np.swapaxes(x_test, 1, 3)
# First attack
clinfm = CarliniLInfMethod(classifier=ptc, targeted=True, max_iter=10, eps=0.5)
params = {'y': random_targets(y_test, ptc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=ptc, targeted=False, max_iter=10, eps=0.5)
x_test_adv = clinfm.generate(x_test)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target != y_pred_adv).any())
def test_classifier_type_check_fail_classifier_LInf(self):
# Use a useless test classifier to test basic classifier properties
class ClassifierNoAPI:
pass
classifier = ClassifierNoAPI
with self.assertRaises(TypeError) as context:
_ = CarliniLInfMethod(classifier=classifier)
self.assertIn(
'For `CarliniLInfMethod` classifier must be an instance of '
'`art.classifiers.classifier.Classifier`, the provided classifier is instance of '
'(<class \'object\'>,).', str(context.exception))
def test_scikitlearn_LInf(self):
from sklearn.linear_model import LogisticRegression
from art.classifiers.scikitlearn import ScikitlearnLogisticRegression
scikitlearn_test_cases = {
LogisticRegression: ScikitlearnLogisticRegression
} # ,
# SVC: ScikitlearnSVC,
# LinearSVC: ScikitlearnSVC}
(_, _), (x_test, y_test) = self.iris
for (model_class, classifier_class) in scikitlearn_test_cases.items():
model = model_class()
classifier = classifier_class(model=model, clip_values=(0, 1))
classifier.fit(x=x_test, y=y_test)
# Test untargeted attack
attack = CarliniLInfMethod(classifier,
targeted=False,
max_iter=10,
eps=0.5)
x_test_adv = attack.generate(x_test)
self.assertFalse((x_test == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(y_test,
axis=1) == predictions_adv).all())
accuracy = np.sum(
predictions_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
logger.info(
'Accuracy of ' + classifier.__class__.__name__ +
' on Iris with C&W adversarial examples: '
'%.2f%%', (accuracy * 100))
# Test targeted attack
targets = random_targets(y_test, nb_classes=3)
attack = CarliniLInfMethod(classifier,
targeted=True,
max_iter=10,
eps=0.5)
x_test_adv = attack.generate(x_test, **{'y': targets})
self.assertFalse((x_test == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertTrue((np.argmax(targets,
axis=1) == predictions_adv).any())
accuracy = np.sum(predictions_adv == np.argmax(
targets, axis=1)) / y_test.shape[0]
logger.info(
'Success rate of ' + classifier.__class__.__name__ +
' on targeted C&W on Iris: %.2f%%', (accuracy * 100))
def test_classifier_type_check_fail_gradients_LInf(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:
_ = CarliniLInfMethod(classifier=classifier)
self.assertIn(
'For `CarliniLInfMethod` classifier must be an instance of '
'`art.classifiers.classifier.ClassifierGradients`, the provided classifier is instance of '
'(<class \'art.classifiers.scikitlearn.ScikitlearnClassifier\'>,).',
str(context.exception))
def test_scikitlearn_LInf(self):
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from art.classifiers.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)
# Test untargeted attack
attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
x_test_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
accuracy = np.sum(predictions_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 C&W adversarial examples: " "%.2f%%",
(accuracy * 100),
)
# Test targeted attack
targets = random_targets(self.y_test_iris, nb_classes=3)
attack = CarliniLInfMethod(classifier, targeted=True, max_iter=10, eps=0.5)
x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
self.assertFalse((self.x_test_iris == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
logger.info(
"Success rate of " + classifier.__class__.__name__ + " on targeted C&W on Iris: %.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 test_tfclassifier(self):
"""
First test with the TFClassifier.
:return:
"""
# Build TFClassifier
tfc, sess = get_classifier_tf()
# Get MNIST
(_, _), (x_test, y_test) = self.mnist
# First attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(np.sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack, no batching
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=False,
max_iter=10,
eps=0.5,
batch_size=1)
x_test_adv = clinfm.generate(x_test)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(np.sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Clean-up session
sess.close()
tf.reset_default_graph()
def test_krclassifier(self):
"""
Second test with the KerasClassifier.
:return:
"""
# Build KerasClassifier
krc, sess = get_classifier_tf()
# Get MNIST
(_, _), (x_test, y_test) = self.mnist
# First attack
clinfm = CarliniLInfMethod(classifier=krc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, krc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(np.sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=krc,
targeted=False,
max_iter=10,
eps=0.5)
x_test_adv = clinfm.generate(x_test)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(np.sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Clean-up
k.clear_session()
def test_tensorflow_mnist_LInf(self):
"""
First test with the TensorFlowClassifier.
:return:
"""
(_, _), (x_test, y_test) = self.mnist
# Build TensorFlowClassifier
tfc, sess = get_classifier_tf(from_logits=True)
# First attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, tfc.nb_classes())}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(np.sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack, no batching
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=False,
max_iter=10,
eps=0.5,
batch_size=1)
x_test_adv = clinfm.generate(x_test)
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(np.sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Clean-up session
sess.close()
def build_adversarial(model, optimizer, loss, input_shape, nb_class, method, batch_size=32, pgd_eps=0.3):
model.eval()
wmodel = PyTorchClassifier(model, loss, optimizer, input_shape, nb_class)
if method == 'deepfool':
adv_crafter = DeepFool(wmodel)
elif method == 'bim':
adv_crafter = BasicIterativeMethod(wmodel, batch_size=batch_size)
elif method == 'jsma':
adv_crafter = SaliencyMapMethod(wmodel, batch_size=batch_size)
elif method == 'cw2':
adv_crafter = CarliniL2Method(wmodel, batch_size=batch_size)
elif method == 'cwi':
adv_crafter = CarliniLInfMethod(wmodel, batch_size=batch_size)
elif method == 'fgsm':
adv_crafter = FastGradientMethod(wmodel, batch_size=batch_size)
elif method == 'pgd':
adv_crafter = ProjectedGradientDescent(wmodel, batch_size=batch_size, eps=pgd_eps)
else:
raise NotImplementedError('Unsupported Attack Method: {}'.format(method))
return adv_crafter
def test_keras_mnist_LInf(self):
"""
Second test with the KerasClassifier.
:return:
"""
# Build KerasClassifier
krc = get_image_classifier_kr(from_logits=True)
# First attack
clinfm = CarliniLInfMethod(classifier=krc, targeted=True, max_iter=10, eps=0.5)
params = {"y": random_targets(self.y_test_mnist, krc.nb_classes())}
x_test_adv = clinfm.generate(self.x_test_mnist, **params)
self.assertFalse((self.x_test_mnist == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.000001)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params["y"], axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug("CW0 Target: %s", target)
logger.debug("CW0 Actual: %s", y_pred_adv)
logger.info("CW0 Success Rate: %.2f", (np.sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=krc, targeted=False, max_iter=10, eps=0.5)
x_test_adv = clinfm.generate(self.x_test_mnist)
self.assertLessEqual(np.amax(x_test_adv), 1.000001)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params["y"], axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug("CW0 Target: %s", target)
logger.debug("CW0 Actual: %s", y_pred_adv)
logger.info("CW0 Success Rate: %.2f", (np.sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Clean-up
k.clear_session()
def test_ptclassifier(self):
"""
Third test with the PyTorchClassifier.
:return:
"""
# Build PyTorchClassifier
ptc = get_classifier_pt()
x_test = np.swapaxes(self.x_test, 1, 3).astype(np.float32)
# First attack
clinfm = CarliniLInfMethod(classifier=ptc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(self.y_test, ptc.nb_classes())}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertLessEqual(np.amax(x_test_adv), 1.0 + 1e-6)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=ptc,
targeted=False,
max_iter=10,
eps=0.5)
x_test_adv = clinfm.generate(x_test)
self.assertLessEqual(np.amax(x_test_adv), 1.0 + 1e-6)
self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target != y_pred_adv).any())
def test_ptclassifier(self):
"""
Third test with the PyTorchClassifier.
:return:
"""
# Get MNIST
(x_train, y_train), (x_test, y_test) = self.mnist
x_train = np.swapaxes(x_train, 1, 3)
x_test = np.swapaxes(x_test, 1, 3)
# Define the network
model = Model()
# Define a loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
# Get classifier
ptc = PyTorchClassifier((0, 1), model, loss_fn, optimizer, (1, 28, 28),
10)
ptc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=10)
# First attack
clinfm = CarliniLInfMethod(classifier=ptc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, ptc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=ptc,
targeted=False,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, ptc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target != y_pred_adv).any())
# Third attack
clinfm = CarliniLInfMethod(classifier=ptc,
targeted=False,
max_iter=10,
eps=0.5)
params = {}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
y_pred = np.argmax(ptc.predict(x_test), axis=1)
y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
self.assertTrue((y_pred != y_pred_adv).any())
def test_tfclassifier(self):
"""
First test with the TFClassifier.
:return:
"""
# Build a TFClassifier
# Define input and output placeholders
input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
output_ph = tf.placeholder(tf.int32, shape=[None, 10])
# Define the tensorflow graph
conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu)
conv = tf.layers.max_pooling2d(conv, 2, 2)
fc = tf.contrib.layers.flatten(conv)
# Logits layer
logits = tf.layers.dense(fc, 10)
# Train operator
loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=output_ph))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train = optimizer.minimize(loss)
# Tensorflow session and initialization
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Get MNIST
(x_train, y_train), (x_test, y_test) = self.mnist
# Train the classifier
tfc = TFClassifier((0, 1), input_ph, logits, output_ph, train, loss,
None, sess)
tfc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=10)
# First attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=False,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Third attack
clinfm = CarliniLInfMethod(classifier=tfc,
targeted=False,
max_iter=10,
eps=0.5)
params = {}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
y_pred = np.argmax(tfc.predict(x_test), axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', y_pred)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(y_pred != y_pred_adv) / float(len(y_pred))))
self.assertTrue((y_pred != y_pred_adv).any())
# First attack without batching
clinfmwob = CarliniLInfMethod(classifier=tfc,
targeted=True,
max_iter=10,
eps=0.5,
batch_size=1)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfmwob.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack without batching
clinfmwob = CarliniLInfMethod(classifier=tfc,
targeted=False,
max_iter=10,
eps=0.5,
batch_size=1)
params = {'y': random_targets(y_test, tfc.nb_classes)}
x_test_adv = clinfmwob.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Third attack without batching
clinfmwob = CarliniLInfMethod(classifier=tfc,
targeted=False,
max_iter=10,
eps=0.5,
batch_size=1)
params = {}
x_test_adv = clinfmwob.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
y_pred = np.argmax(tfc.predict(x_test), axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', y_pred)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(y_pred != y_pred_adv) / float(len(y_pred))))
self.assertTrue((y_pred != y_pred_adv).any())
def test_krclassifier(self):
"""
Second test with the KerasClassifier.
:return:
"""
# Initialize a tf session
session = tf.Session()
k.set_session(session)
# Get MNIST
(x_train, y_train), (x_test, y_test) = self.mnist
# Create simple CNN
model = Sequential()
model.add(
Conv2D(4,
kernel_size=(5, 5),
activation='relu',
input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(lr=0.01),
metrics=['accuracy'])
# Get classifier
krc = KerasClassifier((0, 1), model, use_logits=False)
krc.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=10)
# First attack
clinfm = CarliniLInfMethod(classifier=krc,
targeted=True,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, krc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack
clinfm = CarliniLInfMethod(classifier=krc,
targeted=False,
max_iter=10,
eps=0.5)
params = {'y': random_targets(y_test, krc.nb_classes)}
x_test_adv = clinfm.generate(x_test, **params)
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
target = np.argmax(params['y'], axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', target)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
# Third attack
clinfm = CarliniLInfMethod(classifier=krc,
targeted=False,
max_iter=10,
eps=0.5)
params = {}
x_test_adv = clinfm.generate(x_test, **params)
self.assertFalse((x_test == x_test_adv).all())
self.assertTrue((x_test_adv <= 1.0001).all())
self.assertTrue((x_test_adv >= -0.0001).all())
y_pred = np.argmax(krc.predict(x_test), axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug('CW0 Target: %s', y_pred)
logger.debug('CW0 Actual: %s', y_pred_adv)
logger.info('CW0 Success Rate: %.2f',
(sum(y_pred != y_pred_adv) / float(len(y_pred))))
self.assertTrue((y_pred != y_pred_adv).any())
def get_adversarial(targeted,
attack_name,
classifier,
xs,
target_ys,
batch_size,
dataset,
fgsm_epsilon=0,
cwl2_confidence=0):
# The attack
attack = ''
samples_range = xs.shape[0]
#======================================
if attack_name == 'FastGradientMethod':
# norm=np.inf, eps=.3, eps_step=0.1, targeted=False, num_random_init=0, batch_size=1,minimal=False
attack = FastGradientMethod(classifier=classifier,
targeted=targeted,
eps=fgsm_epsilon,
batch_size=batch_size)
#=====================================
elif attack_name == 'CarliniLInfMethod':
# confidence=0.0, targeted=False, learning_rate=0.01, max_iter=10, max_halving=5,
#max_doubling=5, eps=0.3, batch_size=128
attack = CarliniLInfMethod(classifier=classifier,
max_iter=1000,
targeted=targeted,
batch_size=batch_size)
#-------------------------------
elif attack_name == 'UniversalPerturbation':
# attacker='deepfool', attacker_params=None, delta=0.2,
# max_iter=20, eps=10.0, norm=np.inf
if targeted:
print('UniversalPerturbation attack cannot be targeted.')
exit()
attack = UniversalPerturbation(classifier=classifier, max_iter=5)
#==============================================
elif attack_name == 'ProjectedGradientDescent':
# norm=np.inf, eps=.3, eps_step=0.1, max_iter=100,
# targeted=False, num_random_init=0, batch_size=1
if dataset == 'mnist':
attack = ProjectedGradientDescent(classifier=classifier,
targeted=targeted,
norm=1,
eps=.3,
eps_step=0.01,
num_random_init=0,
max_iter=40,
batch_size=batch_size)
else:
attack = ProjectedGradientDescent(classifier=classifier,
targeted=targeted,
norm=1,
eps=8.0,
eps_step=2.0,
num_random_init=0,
max_iter=7,
batch_size=batch_size)
if targeted:
# Generate the adversarial samples in steps
adv = attack.generate(xs[0:batch_size, :, :, :],
y=target_ys[0:batch_size]) ###################
last_ii = 0
for ii in range(batch_size, samples_range - batch_size, batch_size):
print(ii)
adv_samples = attack.generate(
xs[ii:ii + batch_size, :, :, :],
y=target_ys[ii:ii + batch_size]) ####################
adv = np.concatenate((adv, adv_samples), axis=0)
last_ii = ii
# The rest of the samples
if last_ii + batch_size < xs.shape[0]:
last_samples = xs[last_ii + batch_size:, :, :, :]
adv_samples = attack.generate(
last_samples,
y=target_ys[last_ii + batch_size:]) ################
adv = np.concatenate((adv, adv_samples), axis=0)
else:
# Generate the adversarial samples in steps
adv = attack.generate(xs[0:batch_size, :, :, :]) ###################
last_ii = 0
for ii in range(batch_size, samples_range - batch_size, batch_size):
print(ii)
adv_samples = attack.generate(
xs[ii:ii + batch_size, :, :, :]) ####################
adv = np.concatenate((adv, adv_samples), axis=0)
last_ii = ii
# The rest of the samples
if last_ii + batch_size < xs.shape[0]:
last_samples = xs[last_ii + batch_size:, :, :, :]
adv_samples = attack.generate(last_samples) ################
adv = np.concatenate((adv, adv_samples), axis=0)
adv = np.asarray(adv)
return adv