  This work attempts to address adversarial robustness of deep networks by
means of novel learning arguments. Specifically, inspired from results in
neuroscience, we propose a local competition principle as a means of
adversarially-robust deep learning. We argue that novel local winner-takes-all
(LWTA) nonlinearities, combined with posterior sampling schemes, can greatly
improve the adversarial robustness of traditional deep networks against
difficult adversarial attack schemes. We combine these LWTA arguments with
tools from the field of Bayesian non-parametrics, specifically the
stick-breaking construction of the Indian Buffet Process, to flexibly account
for the inherent uncertainty in data-driven modeling. As we experimentally
show, the new proposed model achieves high robustness to adversarial
perturbations on MNIST and CIFAR10 datasets. Our model achieves
state-of-the-art results in powerful white-box attacks, while at the same time
retaining its benign accuracy to a high degree. Equally importantly, our
approach achieves this result while requiring far less trainable model
parameters than the existing state-of-the-art.
