On the Adversarial Robustness of Generative Autoencoders in the Latent Space

TL;DR

This paper investigates the vulnerability of generative autoencoders, like VAEs, to adversarial attacks in the latent space, revealing their weaknesses and exploring methods to improve robustness.

Contribution

First comprehensive study on the adversarial robustness of generative autoencoders in the latent space, including empirical attacks, comparisons, and robustness enhancement strategies.

Findings

VAEs are vulnerable to latent space attacks

Deterministic autoencoders show better robustness than VAEs

Adversarial training can improve latent robustness

Abstract

The generative autoencoders, such as the variational autoencoders or the adversarial autoencoders, have achieved great success in lots of real-world applications, including image generation, and signal communication. However, little concern has been devoted to their robustness during practical deployment. Due to the probabilistic latent structure, variational autoencoders (VAEs) may confront problems such as a mismatch between the posterior distribution of the latent and real data manifold, or discontinuity in the posterior distribution of the latent. This leaves a back door for malicious attackers to collapse VAEs from the latent space, especially in scenarios where the encoder and decoder are used separately, such as communication and compressed sensing. In this work, we provide the first study on the adversarial robustness of generative autoencoders in the latent space. Specifically, we empirically demonstrate the latent vulnerability of popular generative autoencoders through attacks in the latent space. We also evaluate the difference between variational autoencoders and their deterministic variants and observe that the latter performs better in latent robustness. Meanwhile, we identify a potential trade-off between the adversarial robustness and the degree of the disentanglement of the latent codes. Additionally, we also verify the feasibility of improvement for the latent robustness of VAEs through adversarial training. In summary, we suggest concerning the adversarial latent robustness of the generative autoencoders, analyze several robustness-relative issues, and give some insights into a series of key challenges.