  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.
