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Certifiably Robust Variational Autoencoders
Ben Barrett · Alexander Camuto · Matthew Willetts · Thomas Rainforth

@
in Deep Generative Models and Downstream Applications »
Event URL: https://openreview.net/forum?id=jjZrTkhh0GZ »

We introduce an approach for training Variational Autoencoders (VAEs) that are certifiably robust to adversarial attack. Specifically, we first derive actionable bounds on the minimal size of an input perturbation required to change a VAE's reconstruction by more than an allowed amount, with these bounds depending on certain key parameters such as the Lipschitz constants of the encoder and decoder. We then show how these parameters can be controlled, thereby providing a mechanism to ensure \textit{a priori} that a VAE will attain a desired level of robustness. Moreover, we extend this to a complete practical approach for training such VAEs to ensure our criteria are met. Critically, our method allows one to specify a desired level of robustness \emph{upfront} and then train a VAE that is guaranteed to achieve this robustness. We further demonstrate that these \emph{Lipschitz--constrained} VAEs are more robust to attack than standard VAEs in practice.

Author Information

Ben Barrett (University of Oxford)
Alexander Camuto (University of Oxford & The Alan Turing Institute)
Matthew Willetts (University College London)
Thomas Rainforth (University of Oxford)

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