text: import foolbox
  import keras
  import numpy as np
  from keras.applications.resnet50 import ResNet50
  import matplotlib.pyplot as plt 
  
  # instantiate model
  keras.backend.set_learning_phase(0)
  kmodel = ResNet50(weights='imagenet')
  preprocessing = (np.array([104, 116, 123]), 1)
  fmodel = foolbox.models.KerasModel(kmodel, bounds=(0, 255), preprocessing=preprocessing)
  
  # get source image and label
  image, label = foolbox.utils.imagenet_example()
  
  # apply attack on source image
  # ::-1 reverses the color channels, because Keras ResNet50 expects BGR instead of RGB
  attack = foolbox.attacks.FGSM(fmodel)
  adversarial = attack(image[:, :, ::-1], label)
  
  plt.figure()
  
  plt.subplot(1, 3, 1)
  plt.title('Original')
  plt.imshow(image / 255)  # division by 255 to convert [0, 255] to [0, 1]
  plt.axis('off')
  
  plt.subplot(1, 3, 2)
  plt.title('Adversarial')
  plt.imshow(adversarial[:, :, ::-1] / 255)  # ::-1 to convert BGR to RGB
  plt.axis('off')
  
  plt.subplot(1, 3, 3)
  plt.title('Difference')
  difference = adversarial[:, :, ::-1] - image
  plt.imshow(difference / abs(difference).max() * 0.2 + 0.5)
  plt.axis('off')
  
  plt.show()
  