Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
Cryptography and Security
2021-05-07 v3 Machine Learning
Machine Learning
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 (-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.
Cite
@article{arxiv.2103.06701,
title = {Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks},
author = {Anna Kuzina and Max Welling and Jakub M. Tomczak},
journal= {arXiv preprint arXiv:2103.06701},
year = {2021}
}