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_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_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_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_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_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_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 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 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()
Exemplo n.º 11
0
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 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()
Exemplo n.º 17
0
    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
Exemplo n.º 18
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    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())
Exemplo n.º 19
0
    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 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 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()
Exemplo n.º 22
0
    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())
Exemplo n.º 23
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    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())
Exemplo n.º 24
0
    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())
                               max_iter=100,
                               eps=0.3)

    for ibatch in range(num_batches):
        bstart = ibatch * eval_batch_size
        bend = min(bstart + eval_batch_size, len(mnist.test.images))

        x_batch = data_test[
            bstart:
            bend, :]  # note that here is the perturbed images not the original images.
        #print(x_batch.shape)
        x_batch = x_batch.reshape((len(x_batch), 28, 28, 1))
        y_batch = labels_test[bstart:bend]

        params = {'y': random_targets(y_batch, tfc.nb_classes)}
        x_batch_adv = clinfm.generate(x_batch, **params)

        #x_batch_adv = attack.perturb(x_batch, y_batch, sess)

        print(x_batch_adv.shape)

        if ibatch == 0:

            adv.append(x_batch_adv)
            adv = np.asarray(adv)
            adv = adv.reshape((500, 784))
            print(adv.shape)
        else:
            adv = np.concatenate((adv, np.asarray(x_batch_adv).reshape(
                (500, 784))),
                                 axis=0)