  In this paper, we consider the problem of assessing the adversarial
robustness of deep neural network models under both Markov chain Monte Carlo
(MCMC) and Bayesian Dark Knowledge (BDK) inference approximations. We
characterize the robustness of each method to two types of adversarial attacks:
the fast gradient sign method (FGSM) and projected gradient descent (PGD). We
show that full MCMC-based inference has excellent robustness, significantly
outperforming standard point estimation-based learning. On the other hand, BDK
provides marginal improvements. As an additional contribution, we present a
storage-efficient approach to computing adversarial examples for large Monte
Carlo ensembles using both the FGSM and PGD attacks.
