MNIST tutorial for the Jacobian-based saliency map approach (JSMA)
  :param train_start: index of first training set example
  :param train_end: index of last training set example
  :param test_start: index of first test set example
  :param test_end: index of last test set example
  :param viz_enabled: (boolean) activate plots of adversarial examples
  :param nb_epochs: number of epochs to train model
  :param batch_size: size of training batches
  :param nb_classes: number of output classes
  :param source_samples: number of test inputs to attack
  :param learning_rate: learning rate for training
  :return: an AccuracyReport object

def mnist_tutorial_jsma(train_start=0, train_end=60000, test_start=0,
                        test_end=10000, viz_enabled=VIZ_ENABLED,
                        nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
                        source_samples=SOURCE_SAMPLES,
                        learning_rate=LEARNING_RATE):
  """
  MNIST tutorial for the Jacobian-based saliency map approach (JSMA)
  :param train_start: index of first training set example
  :param train_end: index of last training set example
  :param test_start: index of first test set example
  :param test_end: index of last test set example
  :param viz_enabled: (boolean) activate plots of adversarial examples
  :param nb_epochs: number of epochs to train model
  :param batch_size: size of training batches
  :param nb_classes: number of output classes
  :param source_samples: number of test inputs to attack
  :param learning_rate: learning rate for training
  :return: an AccuracyReport object
  """
  # Object used to keep track of (and return) key accuracies
  report = AccuracyReport()

  # Set TF random seed to improve reproducibility
  tf.set_random_seed(1234)

  # Create TF session and set as Keras backend session
  sess = tf.Session()
  print("Created TensorFlow session.")

  set_log_level(logging.DEBUG)

  # Get MNIST test data
  mnist = MNIST(train_start=train_start, train_end=train_end,
                test_start=test_start, test_end=test_end)
  x_train, y_train = mnist.get_set('train')
  x_test, y_test = mnist.get_set('test')

  # Obtain Image Parameters
  img_rows, img_cols, nchannels = x_train.shape[1:4]
  nb_classes = y_train.shape[1]

  # Define input TF placeholder
  x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols,
                                        nchannels))
  y = tf.placeholder(tf.float32, shape=(None, nb_classes))

  nb_filters = 64
  # Define TF model graph
  model = ModelBasicCNN('model1', nb_classes, nb_filters)
  preds = model.get_logits(x)
  loss = CrossEntropy(model, smoothing=0.1)
  print("Defined TensorFlow model graph.")

  ###########################################################################
  # Training the model using TensorFlow
  ###########################################################################

  # Train an MNIST model
  train_params = {
      'nb_epochs': nb_epochs,
      'batch_size': batch_size,
      'learning_rate': learning_rate
  }
  sess.run(tf.global_variables_initializer())
  rng = np.random.RandomState([2017, 8, 30])
  train(sess, loss, x_train, y_train, args=train_params, rng=rng)

  # Evaluate the accuracy of the MNIST model on legitimate test examples
  eval_params = {'batch_size': batch_size}
  accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
  assert x_test.shape[0] == test_end - test_start, x_test.shape
  print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
  report.clean_train_clean_eval = accuracy

  ###########################################################################
  # Craft adversarial examples using the Jacobian-based saliency map approach
  ###########################################################################
  print('Crafting ' + str(source_samples) + ' * ' + str(nb_classes - 1) +
        ' adversarial examples')

  # Keep track of success (adversarial example classified in target)
  results = np.zeros((nb_classes, source_samples), dtype='i')

  # Rate of perturbed features for each test set example and target class
  perturbations = np.zeros((nb_classes, source_samples), dtype='f')

  # Initialize our array for grid visualization
  grid_shape = (nb_classes, nb_classes, img_rows, img_cols, nchannels)
  grid_viz_data = np.zeros(grid_shape, dtype='f')

  # Instantiate a SaliencyMapMethod attack object
  jsma = SaliencyMapMethod(model, sess=sess)
  jsma_params = {'theta': 1., 'gamma': 0.1,
                 'clip_min': 0., 'clip_max': 1.,
                 'y_target': None}

  figure = None
  # Loop over the samples we want to perturb into adversarial examples
  for sample_ind in xrange(0, source_samples):
    print('--------------------------------------')
    print('Attacking input %i/%i' % (sample_ind + 1, source_samples))
    sample = x_test[sample_ind:(sample_ind + 1)]

    # We want to find an adversarial example for each possible target class
    # (i.e. all classes that differ from the label given in the dataset)
    current_class = int(np.argmax(y_test[sample_ind]))
    target_classes = other_classes(nb_classes, current_class)

    # For the grid visualization, keep original images along the diagonal
    grid_viz_data[current_class, current_class, :, :, :] = np.reshape(
        sample, (img_rows, img_cols, nchannels))

    # Loop over all target classes
    for target in target_classes:
      print('Generating adv. example for target class %i' % target)

      # This call runs the Jacobian-based saliency map approach
      one_hot_target = np.zeros((1, nb_classes), dtype=np.float32)
      one_hot_target[0, target] = 1
      jsma_params['y_target'] = one_hot_target
      adv_x = jsma.generate_np(sample, **jsma_params)

      # Check if success was achieved
      res = int(model_argmax(sess, x, preds, adv_x) == target)

      # Computer number of modified features
      adv_x_reshape = adv_x.reshape(-1)
      test_in_reshape = x_test[sample_ind].reshape(-1)
      nb_changed = np.where(adv_x_reshape != test_in_reshape)[0].shape[0]
      percent_perturb = float(nb_changed) / adv_x.reshape(-1).shape[0]

      # Display the original and adversarial images side-by-side
      if viz_enabled:
        figure = pair_visual(
            np.reshape(sample, (img_rows, img_cols, nchannels)),
            np.reshape(adv_x, (img_rows, img_cols, nchannels)), figure)

      # Add our adversarial example to our grid data
      grid_viz_data[target, current_class, :, :, :] = np.reshape(
          adv_x, (img_rows, img_cols, nchannels))

      # Update the arrays for later analysis
      results[target, sample_ind] = res
      perturbations[target, sample_ind] = percent_perturb

  print('--------------------------------------')

  # Compute the number of adversarial examples that were successfully found
  nb_targets_tried = ((nb_classes - 1) * source_samples)
  succ_rate = float(np.sum(results)) / nb_targets_tried
  print('Avg. rate of successful adv. examples {0:.4f}'.format(succ_rate))
  report.clean_train_adv_eval = 1. - succ_rate

  # Compute the average distortion introduced by the algorithm
  percent_perturbed = np.mean(perturbations)
  print('Avg. rate of perturbed features {0:.4f}'.format(percent_perturbed))

  # Compute the average distortion introduced for successful samples only
  percent_perturb_succ = np.mean(perturbations * (results == 1))
  print('Avg. rate of perturbed features for successful '
        'adversarial examples {0:.4f}'.format(percent_perturb_succ))

  # Close TF session
  sess.close()

  # Finally, block & display a grid of all the adversarial examples
  if viz_enabled:
    import matplotlib.pyplot as plt
    plt.close(figure)
    _ = grid_visual(grid_viz_data)

  return report