mlsanityData to 14 Oct 2026

Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks

arXiv 2103.06701 ↗First posted 10 Mar 2021cs.CR cs.LG stat.MLcited by 7

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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.

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

12 of 20 references.

References · 12

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  4. 2018Variational image compression with a scale hyperpriorcited by 935
  5. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 637
  6. 2015Importance Weighted Autoencoderscited by 456
  7. 2016Ladder Variational Autoencoderscited by 318
  8. 2020Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Imagescited by 264
  9. 2019BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modelingcited by 109
  10. 2015Hierarchical Variational Modelscited by 89
  11. 2018Adversarial Attacks on Variational Autoencoderscited by 19
  12. 2020Towards a Theoretical Understanding of the Robustness of Variational Autoencoderscited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 0; 2025: 1; 2026: 1.

7 papers.

  1. 2022Alleviating Adversarial Attacks on Variational Autoencoders with MCMCcited by 10
  2. 2021A Closer Look at the Adversarial Robustness of Information Bottleneck Modelscited by 4
  3. 2022Adversarial robustness of VAEs through the lens of local geometrycited by 4
  4. 2023Adversarial Robustness in Unsupervised Machine Learning: A Systematic Reviewcited by 3
  5. 2023On the Adversarial Robustness of Generative Autoencoders in the Latent Spacecited by 0
  6. 2025Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restorationcited by 0
  7. 2026On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspacescited by 0