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

Understand

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.