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arXiv · 2112.02671

Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness

Konstantinos P. Panousis·Sotirios Chatzis·Sergios Theodoridis
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Abstract

This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings. In our work, we replace the conventional ReLU-based nonlinearities with blocks comprising locally and stochastically competing linear units. The output of each network layer now yields a sparse output, depending on the outcome of winner sampling in each block. We rely on the Variational Bayesian framework for training and inference; we incorporate conventional PGD-based adversarial training arguments to increase the overall adversarial robustness. As we experimentally show, the arising networks yield state-of-the-art robustness against powerful adversarial attacks while retaining very high classification rate in the benign case.

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Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis. 2021-12-05. Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness. https://arxiv.org/abs/2112.02671

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