Summary of Sparse Patches Adversarial Attacks Via Extrapolating Point-wise Information, by Yaniv Nemcovsky et al.
Sparse patches adversarial attacks via extrapolating point-wise information
by Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Sparse and patch adversarial attacks, a security risk to autonomous systems, were previously shown to be applicable in realistic settings. This paper proposes a novel approach for sparse patches adversarial attacks via point-wise trimming dense adversarial perturbations. The method enables simultaneous optimization of multiple sparse patches’ locations and perturbations for any given number and shape. This approach also improves the state-of-the-art over multiple extensive settings, including standard sparse adversarial attacks. The proposed method is applicable to both patch and standard sparse adversarial attacks, making it a powerful tool for evaluating the robustness of autonomous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous systems are at risk from attacks that can cause them to malfunction or make wrong decisions. These attacks can be very good at fooling the system into doing something it shouldn’t do. This paper proposes a new way to create these attacks, which makes them more powerful and harder to detect. The new method is called “point-wise trimming dense adversarial perturbations”. It allows attackers to create multiple small areas of attack that can be tailored to specific parts of the system. |
Keywords
* Artificial intelligence * Optimization
