Improving VAEs' Robustness to Adversarial Attack

Understand

Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein they are fooled into reconstructing a chosen target image.

• However, how to defend against such attacks remains an open problem.
• We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs.
• Namely, we first demonstrate that methods proposed to obtain disentangled latent representations produce VAEs that are more robust to these attacks.