  Convolutional neural networks are vulnerable to small $\ell^p$ adversarial
attacks, while the human visual system is not. Inspired by neural networks in
the eye and the brain, we developed a novel artificial neural network model
that recurrently collects data with a log-polar field of view that is
controlled by attention. We demonstrate the effectiveness of this design as a
defense against SPSA and PGD adversarial attacks. It also has beneficial
properties observed in the animal visual system, such as reflex-like pathways
for low-latency inference, fixed amount of computation independent of image
size, and rotation and scale invariance. The code for experiments is available
at https://gitlab.com/exwzd-public/kiritani_ono_2020.
