def test_iris_pt(self): (_, _), (x_test, y_test) = self.iris classifier = get_iris_classifier_pt() attack_params = { "max_iter": 1, "attacker": "ead", "attacker_params": { "max_iter": 5, "targeted": False } } attack = UniversalPerturbation(classifier) attack.set_params(**attack_params) 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 universal 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_params = { "max_iter": 1, "attacker": "newtonfool", "attacker_params": { "max_iter": 5 } } attack = UniversalPerturbation(classifier) attack.set_params(**attack_params) 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 universal adversarial examples: %.2f%%', (acc * 100))