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A recent paper by Liu et al. combines the topics of adversarial training andBayesian Neural Networks (BNN) and suggests that adversarially trained BNNs aremore robust against adversarial attacks than their non-Bayesian counterparts.Here, I analyze the proposed defense and suggest that one needs to adjust theadversarial attack to incorporate the stochastic nature of a Bayesian networkto perform an accurate evaluation of its robustness. Using this new type ofattack I show that there appears to be no strong evidence for higher robustnessof the adversarially trained BNNs.

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