Performing an adversarial attack on images

In the previous section, we learned about generating an image from random noise using a VAE. However, it was an unsupervised exercise. What if we want to modify an image in such a way that the change in image is so minimal that it is indistinguishable from the original image for a human, but still the neural network model perceives the object as belonging to a different class? Adversarial attacks on images come in handy in such a scenario.

Adversarial attacks refer to the changes that we make to input image values (pixels) so that we meet a certain objective.

In this section, we will learn about modifying an image slightly in such a way that the pre-trained models now predict them as a different class (specified by the user) and not the original class. The strategy we will adopt is as follows:

1. Provide an image of an elephant.
2. Specify the target class corresponding to the image.
3. Import a pre-trained model where the parameters of the model are...