# Revisiting Role of Autoencoders in Adversarial Settings

arXiv: https://arxiv.org/abs/2005.10750 · HTML: https://mlsanity.com/paper/2005.10750/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 2 of 2)

- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
- [Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning](https://mlsanity.com/paper/2306.15457/) (2023)

## Abstract (arXiv)

To combat against adversarial attacks, autoencoder structure is widely used to perform denoising which is regarded as gradient masking. In this paper, we revisit the role of autoencoders in adversarial settings. Through the comprehensive experimental results and analysis, this paper presents the inherent property of adversarial robustness in the autoencoders. We also found that autoencoders may use robust features that cause inherent adversarial robustness. We believe that our discovery of the adversarial robustness of the autoencoders can provide clues to the future research and applications for adversarial defense.