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arXiv · 2103.06701

Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks

Anna Kuzina·Max Welling·Jakub M. Tomczak
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Abstract

In this work, we explore adversarial attacks on the Variational Autoencoders (VAE). We show how to modify data point to obtain a prescribed latent code (supervised attack) or just get a drastically different code (unsupervised attack). We examine the influence of model modifications ($\beta$-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.

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Anna Kuzina, Max Welling, Jakub M. Tomczak. 2021-03-10. Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks. https://arxiv.org/abs/2103.06701

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