Summary of Position: Towards Resilience Against Adversarial Examples, by Sihui Dai et al.
Position: Towards Resilience Against Adversarial Examples
by Sihui Dai, Chong Xiang, Tong Wu, Prateek Mittal
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 proposes a new approach to defending against adversarial examples, arguing that current research focuses too much on achieving robustness against a single type of attack. Instead, the authors suggest developing defense algorithms that are not only robust but also adversarially resilient, able to quickly adapt to new attacks. The concept of adversarial resilience is defined, and considerations for designing such defenses are outlined. The paper introduces the subproblem of continual adaptive robustness, where the defender gains knowledge of possible perturbation spaces over time and updates their model accordingly. This approach is connected to previously studied problems of multiattack robustness and unforeseen attack robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer models more secure against bad data. Right now, most researchers are trying to make sure models can withstand one type of bad data. But there’s a lot more out there! The authors think we should focus on creating models that can adapt quickly if new types of bad data come along. They explain what this means and how it could work. |
