# A Closer Look at the Adversarial Robustness of Information Bottleneck Models

arXiv: https://arxiv.org/abs/2107.05712 · HTML: https://mlsanity.com/paper/2107.05712/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Robustness via curvature regularization, and vice versa](https://mlsanity.com/paper/1811.09716/) (2018)
- [Disentangling Adversarial Robustness and Generalization](https://mlsanity.com/paper/1812.00740/) (2018)
- [Adversarial Robustness through Local Linearization](https://mlsanity.com/paper/1907.02610/) (2019)
- [An Alternative Surrogate Loss for PGD-based Adversarial Testing](https://mlsanity.com/paper/1910.09338/) (2019)
- [Towards a Theoretical Understanding of the Robustness of Variational Autoencoders](https://mlsanity.com/paper/2007.07365/) (2020)
- [Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning](https://mlsanity.com/paper/2003.12537/) (2020)
- [CEB Improves Model Robustness](https://mlsanity.com/paper/2002.05380/) (2020)
- [Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks](https://mlsanity.com/paper/2103.06701/) (2021)

## Built on by (most cited first, 4 of 4)

- [Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network](https://mlsanity.com/paper/2210.14229/) (2022)
- [Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information](https://mlsanity.com/paper/2207.05756/) (2022)
- [Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses](https://mlsanity.com/paper/2407.04486/) (2024)
- [When Interpretability Becomes a Liability: Adversarial Attacks on CBM Concept Layers](https://mlsanity.com/paper/2605.25304/) (2026)

## Abstract (arXiv)

We study the adversarial robustness of information bottleneck models for classification. Previous works showed that the robustness of models trained with information bottlenecks can improve upon adversarial training. Our evaluation under a diverse range of white-box l∞ attacks suggests that information bottlenecks alone are not a strong defense strategy, and that previous results were likely influenced by gradient obfuscation.