text: # MIT License
  #
  # Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
  # documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
  # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
  # persons to whom the Software is furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
  # Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
  # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
  # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  """
  This module tests the Pixel Attack.
  The Pixel Attack is a generalisation of One Pixel Attack.
  
  | One Pixel Attack Paper link:
      https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
      (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
  | Pixel Attack Paper link:
      https://arxiv.org/abs/1906.06026
  """
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import logging
  import unittest
  
  import numpy as np
  
  from art.attacks.evasion.pixel_threshold import PixelAttack
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import get_labels_np_array
  
  from tests.utils import TestBase
  from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  
  logger = logging.getLogger(__name__)
  
  
  class TestPixelAttack(TestBase):
      """
      A unittest class for testing the Pixel Attack.
  
      This module tests the Pixel Attack.
      The Pixel Attack is a generalisation of One Pixel Attack.
  
      | One Pixel Attack Paper link:
          https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
          (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
      | Pixel Attack Paper link:
          https://arxiv.org/abs/1906.06026
      """
  
      @classmethod
      def setUpClass(cls):
          super().setUpClass()
  
          cls.n_test = 2
          cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
          cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
  
      def test_6_keras_mnist(self):
          """
          Test with the KerasClassifier. (Untargeted Attack)
          :return:
          """
  
          classifier = get_image_classifier_kr()
          self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
  
      def test_2_tensorflow_mnist(self):
          """
          Test with the TensorFlowClassifier. (Untargeted Attack)
          :return:
          """
          classifier, sess = get_image_classifier_tf()
          self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
  
      def test_4_pytorch_mnist(self):
          """
          Test with the PyTorchClassifier. (Untargeted Attack)
          :return:
          """
          x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
          classifier = get_image_classifier_pt()
          self._test_attack(classifier, x_test, self.y_test_mnist, False)
  
      def test_7_keras_mnist_targeted(self):
          """
          Test with the KerasClassifier. (Targeted Attack)
          :return:
          """
          classifier = get_image_classifier_kr()
          self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
  
      def test_3_tensorflow_mnist_targeted(self):
          """
          Test with the TensorFlowClassifier. (Targeted Attack)
          :return:
          """
          classifier, sess = get_image_classifier_tf()
          self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
  
      def test_5_pytorch_mnist_targeted(self):
          """
          Test with the PyTorchClassifier. (Targeted Attack)
          :return:
          """
          x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
          classifier = get_image_classifier_pt()
          self._test_attack(classifier, x_test, self.y_test_mnist, True)
  
      def _test_attack(self, classifier, x_test, y_test, targeted):
          """
          Test with the Pixel Attack
          :return:
          """
          x_test_original = x_test.copy()
  
          if targeted:
              # Generate random target classes
              class_y_test = np.argmax(y_test, axis=1)
              nb_classes = np.unique(class_y_test).shape[0]
              targets = np.random.randint(nb_classes, size=self.n_test)
              for i in range(self.n_test):
                  if class_y_test[i] == targets[i]:
                      targets[i] -= 1
          else:
              targets = y_test
  
          for es in [1]:  # Option 0 is not easy to reproduce reliably, we should consider it at a later time
              df = PixelAttack(classifier, th=64, es=es, targeted=targeted, verbose=False)
              x_test_adv = df.generate(x_test_original, targets, max_iter=10)
  
              np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, x_test, x_test_adv)
              self.assertFalse((0.0 == x_test_adv).all())
  
              y_pred = get_labels_np_array(classifier.predict(x_test_adv))
  
              accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
              logger.info("Accuracy on adversarial examples: %.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 - x_test))), 0.0, delta=0.00001)
  
      def test_1_classifier_type_check_fail(self):
          backend_test_classifier_type_check_fail(PixelAttack, [BaseEstimator, NeuralNetworkMixin, ClassifierMixin])
  
  
  if __name__ == "__main__":
      unittest.main()
  