# Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks

arXiv: https://arxiv.org/abs/2103.06701 · HTML: https://mlsanity.com/paper/2103.06701/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images](https://mlsanity.com/paper/2011.10650/) (2020)
- [BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling](https://mlsanity.com/paper/1902.02102/) (2019)
- [Hierarchical Variational Models](https://mlsanity.com/paper/1511.02386/) (2015)
- [Adversarial Attacks on Variational Autoencoders](https://mlsanity.com/paper/1806.04646/) (2018)
- [Towards a Theoretical Understanding of the Robustness of Variational Autoencoders](https://mlsanity.com/paper/2007.07365/) (2020)

## Built on by (most cited first, 7 of 7)

- [Alleviating Adversarial Attacks on Variational Autoencoders with MCMC](https://mlsanity.com/paper/2203.09940/) (2022)
- [A Closer Look at the Adversarial Robustness of Information Bottleneck Models](https://mlsanity.com/paper/2107.05712/) (2021)
- [Adversarial robustness of VAEs through the lens of local geometry](https://mlsanity.com/paper/2208.03923/) (2022)
- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
- [On the Adversarial Robustness of Generative Autoencoders in the Latent Space](https://mlsanity.com/paper/2307.02202/) (2023)
- [Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restoration](https://mlsanity.com/paper/2505.03646/) (2025)
- [On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces](https://mlsanity.com/paper/2607.07375/) (2026)

## Abstract (arXiv)

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 (β-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.