Summary of Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches, by Lingxuan Wu et al.
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
by Lingxuan Wu, Xiao Yang, Yinpeng Dong, Liuwei Xie, Hang Su, Jun Zhu
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to defending deep neural networks against adversarial patches in 3D real-world settings. The Embodied Active Defense (EAD) strategy actively contextualizes environmental information to address misaligned adversarial patches, using two recurrent sub-modules: perception and policy. These modules process beliefs and observations to refine comprehension of the target object and develop strategic actions. To optimize learning efficiency, the paper incorporates a differentiable approximation of environmental dynamics and deploys attack-agnostic patches. Experimental results show that EAD enhances robustness against various patches without compromising standard accuracy, demonstrating excellent generalization to unseen attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to protect computers from bad inputs that can trick them into making mistakes. It creates an active defense system that uses information about the environment to stop these bad inputs. This system has two parts: one that understands what’s happening in the environment and another that decides how to respond. The system is very good at stopping bad inputs, even if it doesn’t know exactly what kind of attack it’s facing. |
Keywords
» Artificial intelligence » Generalization
