The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks

Arno Blaas; Stephen J. Roberts

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arXiv · 2101.02689

The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks

Arno Blaas·Stephen J. Roberts

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Abstract

It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for Bayesian models, as they are well-suited for safety-critical applications, in which adversarial attacks can have catastrophic outcomes. In this work, we take a deeper look at the adversarial robustness of Bayesian Neural Networks (BNNs). In particular, we consider whether the adversarial robustness of a BNN can be increased by model choices, particularly the Lipschitz continuity induced by the prior. Conducting in-depth analysis on the case of i.i.d., zero-mean Gaussian priors and posteriors approximated via mean-field variational inference, we find evidence that adversarial robustness is indeed sensitive to the prior variance.

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Arno Blaas, Stephen J. Roberts. 2021-01-07. The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks. https://arxiv.org/abs/2101.02689

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