  Adversarial attacks can affect the object recognition capabilities of
machines in wild. These can often result from spurious correlations between
input and class labels, and are prone to memorization in large networks. While
networks are expected to do automated feature selection, it is not effective at
the scale of the object. Humans, however, are able to select the minimum set of
features required to form a robust representation of an object. In this work,
we show that finetuning any pretrained off-the-shelf network with Extreme Image
Transformations (EIT) not only helps in learning a robust latent
representation, it also improves the performance of these networks against
common adversarial attacks of various intensities. Our EIT trained networks
show strong activations in the object regions even when tested with more
intense noise, showing promising generalizations across different kinds of
adversarial attacks.
