Provably robust deep generative models

Understand

Recent work in adversarial attacks has developed provably robust methods for training deep neural network classifiers.

• However, although they are often mentioned in the context of robustness, deep generative models themselves have received relatively little attention in terms of formally analyzing their robustness properties.
• In this paper, we propose a method for training provably robust generative models, specifically a provably robust version of the variational auto-encoder (VAE).
• To do so, we first formally define a (certifiably) robust lower bound on the variational lower bound of the likelihood, and then show how this bound can be optimized during training to produce a robust VAE.