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_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_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))