  Classifiers based on deep neural networks are susceptible to adversarial
attack, where the widely existing vulnerability has invoked the research in
defending them from potential threats. Given a vulnerable classifier, existing
defense methods are mostly white-box and often require re-training the victim
under modified loss functions/training regimes. While the model/data/training
specifics of the victim are usually unavailable to the user, re-training is
unappealing, if not impossible for reasons such as limited computational
resources. To this end, we propose a new post-train black-box defense
framework. It can turn any pre-trained classifier into a resilient one with
little knowledge of the model specifics. This is achieved by new joint Bayesian
treatments on the clean data, the adversarial examples and the classifier, for
maximizing their joint probability. It is further equipped with a new
post-train strategy which keeps the victim intact, avoiding re-training. We
name our framework Bayesian Boundary Correction (BBC). BBC is a general and
flexible framework that can easily adapt to different data types. We
instantiate BBC for image classification and skeleton-based human activity
recognition, for both static and dynamic data. Exhaustive evaluation shows that
BBC has superior robustness and can enhance robustness without severely hurting
the clean accuracy, compared with existing defense methods.
