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. However, this robustness comes at
the cost of reducing the quality of the reconstructions. We ameliorate this by
applying disentangling methods to hierarchical VAEs. The resulting models
produce high-fidelity autoencoders that are also adversarially robust. We
confirm their capabilities on several different datasets and with current
state-of-the-art VAE adversarial attacks, and also show that they increase the
robustness of downstream tasks to attack.

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