Biologically Inspired Mechanisms for Adversarial Robustness

Manish Reddy Vuyyuru, Andrzej Banburski, Nishka Pant, tomaso a poggio

Abstract:

A convolutional neural network strongly robust to adversarial perturbations at
reasonable computational and performance cost has not yet been demonstrated.
The primate visual ventral stream seems to be robust to small perturbations in
visual stimuli but the underlying mechanisms that give rise to this robust perception
are not understood. In this work, we investigate the role of two biologically
plausible mechanisms in adversarial robustness. We demonstrate that the nonuniform sampling performed by the primate retina and the presence of multiple
receptive fields with a range of receptive field sizes at each eccentricity improve
the robustness of neural networks to small adversarial perturbations. We verify
that these two mechanisms do not suffer from gradient obfuscation and study their
contribution to adversarial robustness through ablation studies.