Summary of Effect Of Ambient-intrinsic Dimension Gap on Adversarial Vulnerability, by Rajdeep Haldar et al.
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
by Rajdeep Haldar, Yue Xing, Qifan Song
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the theoretical underpinnings of adversarial attacks on machine learning models, specifically the distinction between natural (on-manifold) and unnatural (off-manifold) attacks. The authors argue that the existence of off-manifold attacks is a consequence of the dimension gap between intrinsic and ambient data dimensions. For 2-layer ReLU networks, they prove that this dimension gap affects vulnerability to adversarial perturbations in the off-manifold direction, without impacting generalization performance on observed data. The main results provide an explicit relationship between attack strength and dimension gap for both on- and off-manifold attacks. This work contributes to understanding the theoretical foundations of adversarial attacks and their implications for machine learning model robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out why some machine learning models are easy to trick into making mistakes, even if they seem perfect when tested normally. The authors look at two types of tricks: ones that a human would notice, called “on-manifold” attacks, and ones that humans wouldn’t detect, called “off-manifold” attacks. They find that the gap between how data is really organized (its “intrinsic dimension”) and how it appears to us (its “ambient dimension”) makes some models more vulnerable to these off-manifold attacks. The authors provide a new understanding of why this happens and how different models are affected. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Relu
