def test_2_tensorflow_failure_attack(self):
"""
Test the corner case when attack fails.
:return:
"""
# Build TensorFlowClassifier
tfc, sess = get_image_classifier_tf()
# Failure attack
ead = ElasticNet(
classifier=tfc,
targeted=True,
max_iter=0,
binary_search_steps=0,
learning_rate=0,
initial_const=1,
verbose=False,
)
params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
x_test_adv = ead.generate(self.x_test_mnist, **params)
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
np.testing.assert_almost_equal(self.x_test_mnist, x_test_adv, 3)
# Clean-up session
if sess is not None:
sess.close()
def test_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)
# Test untargeted attack
attack = ElasticNet(classifier, targeted=False, max_iter=2)
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 = 1.0 - np.sum(predictions_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info(
"EAD success rate of " + classifier.__class__.__name__ +
" on Iris: %.2f%%", (accuracy * 100))
# Test targeted attack
targets = random_targets(self.y_test_iris, nb_classes=3)
attack = ElasticNet(classifier, targeted=True, max_iter=2)
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(
"Targeted EAD success rate of " +
classifier.__class__.__name__ + " 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_check_params(self):
ptc = get_image_classifier_pt(from_logits=True)
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, binary_search_steps="1.0")
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, binary_search_steps=-1)
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, max_iter="1.0")
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, max_iter=-1)
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, batch_size="1.0")
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, batch_size=-1)
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, decision_rule=1.0)
with self.assertRaises(ValueError):
_ = ElasticNet(ptc, verbose="True")
def test_tensorflow_mnist(self):
"""
First test with the TensorFlowClassifier.
:return:
"""
x_test_original = self.x_test_mnist.copy()
# Build TensorFlowClassifier
tfc, sess = get_image_classifier_tf(from_logits=True)
# First attack
ead = ElasticNet(classifier=tfc, targeted=True, max_iter=2)
params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
x_test_adv = ead.generate(self.x_test_mnist, **params)
expected_x_test_adv = np.asarray([
0.45704955,
0.43627003,
0.57238287,
1.0,
0.11541145,
0.12619308,
0.48318917,
0.3457903,
0.17863746,
0.09060935,
0.0,
0.00963121,
0.0,
0.04749763,
0.4058206,
0.17860745,
0.0,
0.9153206,
0.84564775,
0.20603634,
0.10586322,
0.00947509,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
np.testing.assert_array_almost_equal(x_test_adv[0, 14, :, 0],
expected_x_test_adv,
decimal=6)
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("EAD target: %s", target)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate on MNIST: %.2f%%",
(100 * sum(target == y_pred_adv) / len(target)))
self.assertTrue((target == y_pred_adv).any())
# Second attack
ead = ElasticNet(classifier=tfc, targeted=False, max_iter=2)
params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
x_test_adv = ead.generate(self.x_test_mnist, **params)
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("EAD target: %s", target)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate on MNIST: %.2f%%",
(100 * sum(target != y_pred_adv) / float(len(target))))
np.testing.assert_array_equal(
y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
# Third attack
ead = ElasticNet(classifier=tfc, targeted=False, max_iter=2)
params = {}
x_test_adv = ead.generate(self.x_test_mnist, **params)
expected_x_test_adv = np.asarray([
0.22866514,
0.21826893,
0.22902338,
0.06268515,
0.0,
0.0,
0.04822975,
0.0,
0.0,
0.0,
0.05555382,
0.0,
0.0,
0.0,
0.38986346,
0.10653087,
0.32385707,
0.98043066,
0.75790393,
0.16486718,
0.16069527,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
np.testing.assert_array_almost_equal(x_test_adv[0, 14, :, 0],
expected_x_test_adv,
decimal=6)
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
y_pred = np.argmax(tfc.predict(self.x_test_mnist), axis=1)
y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
logger.debug("EAD target: %s", y_pred)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate: %.2f%%",
(100 * sum(y_pred != y_pred_adv) / float(len(y_pred))))
np.testing.assert_array_equal(
y_pred_adv, np.asarray([0, 4, 7, 9, 0, 7, 7, 3, 0, 7]))
# First attack without batching
ead_wob = ElasticNet(classifier=tfc,
targeted=True,
max_iter=2,
batch_size=1)
params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
x_test_adv = ead_wob.generate(self.x_test_mnist, **params)
expected_x_test_adv = np.asarray([
0.3287169,
0.31374657,
0.42853343,
0.8994576,
0.19850709,
0.11997936,
0.5622535,
0.43854535,
0.19387433,
0.12516324,
0.0,
0.10933565,
0.02162433,
0.07120894,
0.95224255,
0.3072921,
0.48966524,
1.0,
0.3814998,
0.15782641,
0.52283823,
0.12852049,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
np.testing.assert_array_almost_equal(x_test_adv[0, 14, :, 0],
expected_x_test_adv,
decimal=6)
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("EAD target: %s", target)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate: %.2f%%",
(100 * sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack without batching
ead_wob = ElasticNet(classifier=tfc,
targeted=False,
max_iter=2,
batch_size=1)
params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
x_test_adv = ead_wob.generate(self.x_test_mnist, **params)
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("EAD target: %s", target)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate: %.2f%%",
(100 * sum(target != y_pred_adv) / float(len(target))))
np.testing.assert_array_equal(
y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
# 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)
# Close session
if sess is not None:
sess.close()
def test_pytorch_iris(self):
classifier = get_tabular_classifier_pt()
attack = ElasticNet(classifier, targeted=False, max_iter=10)
x_test_adv = attack.generate(self.x_test_iris.astype(np.float32))
expected_x_test_adv = np.asarray(
[0.8479194, 0.42525578, 0.70166135, 0.28664517])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
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)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
1,
2,
2,
2,
1,
1,
1,
2,
1,
2,
1,
1,
1,
2,
2,
2,
2,
2,
1,
1,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
2,
1,
2,
1,
0,
2,
2,
1,
2,
0,
2,
2,
1,
1,
2,
]),
)
accuracy = 1.0 - np.sum(predictions_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
def test_tensorflow_iris(self):
classifier, _ = get_tabular_classifier_tf()
# Test untargeted attack
attack = ElasticNet(classifier, targeted=False, max_iter=10)
x_test_adv = attack.generate(self.x_test_iris)
expected_x_test_adv = np.asarray(
[0.8479195, 0.42525578, 0.70166135, 0.28664514])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
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)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
1,
2,
2,
2,
1,
1,
1,
2,
1,
2,
1,
1,
1,
2,
2,
2,
2,
2,
1,
1,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
2,
1,
2,
1,
0,
2,
2,
1,
2,
0,
2,
2,
1,
1,
2,
]),
)
accuracy = 1.0 - np.sum(predictions_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
# Test targeted attack
targets = random_targets(self.y_test_iris, nb_classes=3)
attack = ElasticNet(classifier, targeted=True, max_iter=10)
x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
expected_x_test_adv = np.asarray(
[0.8859426, 0.51877, 0.5014498, 0.05447771])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
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)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
0,
0,
0,
2,
2,
2,
2,
2,
2,
2,
2,
2,
0,
2,
0,
0,
2,
2,
0,
2,
2,
2,
2,
2,
2,
0,
0,
0,
2,
0,
2,
2,
2,
2,
2,
0,
0,
0,
2,
2,
2,
2,
2,
0,
2,
]),
)
accuracy = np.sum(predictions_adv == np.argmax(
targets, axis=1)) / self.y_test_iris.shape[0]
logger.info("Targeted EAD success rate on Iris: %.2f%%",
(accuracy * 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,
channels_first=True)
attack = ElasticNet(classifier, targeted=False, max_iter=10)
x_test_adv = attack.generate(self.x_test_iris)
expected_x_test_adv = np.asarray(
[0.85931635, 0.44633555, 0.65658355, 0.23840423])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
1,
1,
1,
2,
1,
1,
1,
2,
1,
2,
1,
1,
1,
2,
1,
1,
2,
2,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
2,
1,
2,
1,
2,
1,
0,
1,
1,
1,
2,
0,
2,
2,
1,
1,
2,
]),
)
accuracy = 1.0 - np.sum(predictions_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
def test_keras_iris_clipped(self):
classifier = get_tabular_classifier_kr()
attack = ElasticNet(classifier, targeted=False, max_iter=10)
x_test_adv = attack.generate(self.x_test_iris)
expected_x_test_adv = np.asarray(
[0.85931635, 0.44633555, 0.65658355, 0.23840423])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
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)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
1,
1,
1,
2,
1,
1,
1,
2,
1,
2,
1,
1,
1,
2,
1,
1,
2,
2,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
2,
1,
2,
1,
2,
1,
0,
1,
1,
1,
2,
0,
2,
2,
1,
1,
2,
]),
)
accuracy = 1.0 - np.sum(predictions_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
def test_pytorch_mnist(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)
x_test_original = x_test.copy()
# Build PyTorchClassifier
ptc = get_image_classifier_pt(from_logits=False)
# First attack
ead = ElasticNet(classifier=ptc, targeted=True, max_iter=2)
params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
x_test_adv = ead.generate(x_test, **params)
expected_x_test_adv = np.asarray([
0.01678124,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.00665895,
0.0,
0.11374763,
0.36250514,
0.5472948,
0.9308808,
1.0,
0.99920374,
0.86274165,
0.6346757,
0.5597227,
0.24191494,
0.25882354,
0.0091916,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
np.testing.assert_array_almost_equal(x_test_adv[2, 0, :, 14],
expected_x_test_adv,
decimal=6)
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(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target == y_pred_adv).any())
# Second attack
ead = ElasticNet(classifier=ptc, targeted=False, max_iter=2)
params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
x_test_adv = ead.generate(x_test, **params)
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(ptc.predict(x_test_adv), axis=1)
self.assertTrue((target != y_pred_adv).any())
np.testing.assert_array_equal(
y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
# 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_keras_mnist(self):
"""
Second test with the KerasClassifier.
:return:
"""
x_test_original = self.x_test_mnist.copy()
# Build KerasClassifier
krc = get_image_classifier_kr()
# First attack
ead = ElasticNet(classifier=krc, targeted=True, max_iter=2)
y_target = to_categorical(np.asarray([6, 6, 7, 4, 9, 7, 9, 0, 1, 0]),
nb_classes=10)
x_test_adv = ead.generate(self.x_test_mnist, y=y_target)
expected_x_test_adv = np.asarray([
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.00183569,
0.0,
0.0,
0.49765405,
1.0,
0.6467149,
0.0033755,
0.0052456,
0.0,
0.01104407,
0.00495547,
0.02747423,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
np.testing.assert_array_almost_equal(x_test_adv[2, 14, :, 0],
expected_x_test_adv,
decimal=6)
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
target = np.argmax(y_target, axis=1)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug("EAD target: %s", target)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate: %.2f%%",
(100 * sum(target == y_pred_adv) / float(len(target))))
self.assertTrue((target == y_pred_adv).any())
# Second attack
ead = ElasticNet(classifier=krc, targeted=False, max_iter=2)
y_target = to_categorical(np.asarray([9, 5, 6, 7, 1, 6, 1, 5, 8, 5]),
nb_classes=10)
x_test_adv = ead.generate(self.x_test_mnist, y=y_target)
self.assertLessEqual(np.amax(x_test_adv), 1.0)
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
logger.debug("EAD target: %s", y_target)
logger.debug("EAD actual: %s", y_pred_adv)
logger.info("EAD success rate: %.2f",
(100 * sum(target != y_pred_adv) / float(len(target))))
self.assertTrue((target != y_pred_adv).any())
np.testing.assert_array_equal(
y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
# 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)
k.clear_session()
def test_3_tensorflow_iris(self):
classifier, sess = get_tabular_classifier_tf()
# Test untargeted attack
attack = ElasticNet(classifier,
targeted=False,
max_iter=10,
verbose=False)
x_test_adv = attack.generate(self.x_test_iris)
expected_x_test_adv = np.asarray(
[0.852286, 0.434626, 0.703376, 0.293738])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
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)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
1,
2,
2,
2,
1,
1,
1,
2,
1,
2,
1,
1,
1,
2,
2,
2,
2,
2,
1,
1,
1,
1,
1,
1,
1,
2,
2,
2,
2,
1,
2,
1,
2,
1,
0,
1,
2,
1,
2,
0,
2,
2,
1,
1,
2,
]),
)
accuracy = 1.0 - np.sum(predictions_adv == np.argmax(
self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
# Test targeted attack
targets = random_targets(self.y_test_iris, nb_classes=3)
attack = ElasticNet(classifier,
targeted=True,
max_iter=10,
verbose=False)
x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
expected_x_test_adv = np.asarray(
[0.892806, 0.531875, 0.501707, 0.059951])
np.testing.assert_array_almost_equal(x_test_adv[0, :],
expected_x_test_adv,
decimal=6)
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)
np.testing.assert_array_equal(
predictions_adv,
np.asarray([
0,
0,
0,
2,
2,
2,
2,
2,
2,
2,
2,
2,
0,
2,
0,
0,
2,
2,
0,
2,
2,
2,
2,
2,
2,
0,
0,
0,
2,
0,
2,
2,
2,
2,
2,
0,
0,
0,
2,
2,
2,
2,
2,
0,
2,
]),
)
accuracy = np.sum(predictions_adv == np.argmax(
targets, axis=1)) / self.y_test_iris.shape[0]
logger.info("Targeted EAD success rate on Iris: %.2f%%",
(accuracy * 100))
# Close session
if sess is not None:
sess.close()
