def test_4_pytorch_iris(self):
classifier = get_tabular_classifier_pt()
attack_params = {
"max_iter": 1,
"attacker": "ead",
"attacker_params": {
"max_iter": 5,
"targeted": False,
"verbose": False
},
}
attack = UniversalPerturbation(classifier, verbose=False)
attack.set_params(**attack_params)
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 universal adversarial examples: %.2f%%",
(acc * 100))
def test_7_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_params = {
"max_iter": 1,
"attacker": "newtonfool",
"attacker_params": {
"max_iter": 5,
"verbose": False
}
}
attack = UniversalPerturbation(classifier, verbose=False)
attack.set_params(**attack_params)
x_test_iris_adv = attack.generate(self.x_test_iris)
self.assertFalse((self.x_test_iris == x_test_iris_adv).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 universal adversarial examples: %.2f%%",
(acc * 100))
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()
# Attack
up = UniversalPerturbation(krc, max_iter=1, attacker="ead", attacker_params={"max_iter": 2, "targeted": False})
x_train_adv = up.generate(self.x_train_mnist)
self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
x_test_adv = self.x_test_mnist + up.noise
self.assertFalse((self.x_test_mnist == x_test_adv).all())
train_y_pred = np.argmax(krc.predict(x_train_adv), axis=1)
test_y_pred = np.argmax(krc.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
# 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 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()
# Attack
up = UniversalPerturbation(tfc, max_iter=1, attacker="newtonfool", attacker_params={"max_iter": 5})
x_train_adv = up.generate(self.x_train_mnist)
self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
x_test_adv = self.x_test_mnist + up.noise
self.assertFalse((self.x_test_mnist == x_test_adv).all())
train_y_pred = np.argmax(tfc.predict(x_train_adv), axis=1)
test_y_pred = np.argmax(tfc.predict(x_test_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
# 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 test_pytorch_mnist(self):
"""
Third test with the PyTorchClassifier.
:return:
"""
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()
# Build PyTorchClassifier
ptc = get_image_classifier_pt()
# Attack
up = UniversalPerturbation(ptc, max_iter=1, attacker="newtonfool", attacker_params={"max_iter": 5})
x_train_mnist_adv = up.generate(x_train_mnist)
self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
x_test_mnist_adv = x_test_mnist + up.noise
self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
train_y_pred = np.argmax(ptc.predict(x_train_mnist_adv), axis=1)
test_y_pred = np.argmax(ptc.predict(x_test_mnist_adv), axis=1)
self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
# 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)
def test_6_keras_iris_clipped(self):
classifier = get_tabular_classifier_kr()
# Test untargeted attack
attack_params = {
"max_iter": 1,
"attacker": "newtonfool",
"attacker_params": {
"max_iter": 5
}
}
attack = UniversalPerturbation(classifier)
attack.set_params(**attack_params)
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 universal adversarial examples: %.2f%%",
(acc * 100))