text: # MIT License
  #
  # Copyright (C) IBM Corporation 2018
  #
  # 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.
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import logging
  import unittest
  
  import numpy as np
  
  from art.attacks.universal_perturbation import UniversalPerturbation
  from art.classifiers import KerasClassifier
  from art.utils import load_dataset, master_seed, get_classifier_tf, get_classifier_kr, get_classifier_pt
  from art.utils import get_iris_classifier_tf, get_iris_classifier_kr, get_iris_classifier_pt
  
  logger = logging.getLogger('testLogger')
  
  BATCH_SIZE = 100
  NB_TRAIN = 500
  NB_TEST = 10
  
  
  class TestUniversalPerturbation(unittest.TestCase):
      """
      A unittest class for testing the UniversalPerturbation attack.
      """
  
      @classmethod
      def setUpClass(cls):
          # Get MNIST
          (x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')
          x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
          x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
          cls.mnist = (x_train, y_train), (x_test, y_test)
  
      def setUp(self):
          # Set master seed
          master_seed(1234)
  
      def test_tfclassifier(self):
          """
          First test with the TFClassifier.
          :return:
          """
          # Build TFClassifier
          tfc, sess = get_classifier_tf()
  
          # Get MNIST
          (x_train, y_train), (x_test, y_test) = self.mnist
  
          # Attack
          up = UniversalPerturbation(tfc, max_iter=1, attacker="newtonfool", attacker_params={"max_iter": 5})
          x_train_adv = up.generate(x_train)
          self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
  
          x_test_adv = x_test + up.noise
          self.assertFalse((x_test == 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(y_test, axis=1) == test_y_pred).all())
          self.assertFalse((np.argmax(y_train, axis=1) == train_y_pred).all())
  
      def test_krclassifier(self):
          """
          Second test with the KerasClassifier.
          :return:
          """
          # Build KerasClassifier
          krc, sess = get_classifier_kr()
  
          # Get MNIST
          (x_train, y_train), (x_test, y_test) = self.mnist
  
          # Attack
          up = UniversalPerturbation(krc, max_iter=1, attacker="ead", attacker_params={"max_iter": 5, "targeted": False})
          x_train_adv = up.generate(x_train)
          self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
  
          x_test_adv = x_test + up.noise
          self.assertFalse((x_test == 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(y_test, axis=1) == test_y_pred).all())
          self.assertFalse((np.argmax(y_train, axis=1) == train_y_pred).all())
  
      def test_ptclassifier(self):
          """
          Third test with the PyTorchClassifier.
          :return:
          """
          # Build PyTorchClassifier
          ptc = get_classifier_pt()
  
          # 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)
  
          # Attack
          up = UniversalPerturbation(ptc, max_iter=1, attacker="newtonfool", attacker_params={"max_iter": 5})
          x_train_adv = up.generate(x_train)
          self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
  
          x_test_adv = x_test + up.noise
          self.assertFalse((x_test == x_test_adv).all())
  
          train_y_pred = np.argmax(ptc.predict(x_train_adv), axis=1)
          test_y_pred = np.argmax(ptc.predict(x_test_adv), axis=1)
          self.assertFalse((np.argmax(y_test, axis=1) == test_y_pred).all())
          self.assertFalse((np.argmax(y_train, axis=1) == train_y_pred).all())
  
  
  class TestUniversalPerturbationVectors(unittest.TestCase):
      @classmethod
      def setUpClass(cls):
          # Get Iris
          (x_train, y_train), (x_test, y_test), _, _ = load_dataset('iris')
          cls.iris = (x_train, y_train), (x_test, y_test)
  
      def setUp(self):
          master_seed(1234)
  
      def test_iris_k_clipped(self):
          (_, _), (x_test, y_test) = self.iris
          classifier, _ = get_iris_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_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))
  
      def test_iris_tf(self):
          (_, _), (x_test, y_test) = self.iris
          classifier, _ = get_iris_classifier_tf()
  
          # Test untargeted attack
          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_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))
  
  
  if __name__ == '__main__':
      unittest.main()
  