  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 non-uniform 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.
