MAGIC uses three collaborating LLM agents to generate scene-aware adversarial patches and place them in real-world images, achieving higher attack success against YOLO and DETR detectors than the natural diffusion attack baseline.
Adversarial Attacks on Traffic Sign Recognition: A Survey
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abstract
Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However, DNNs are known to be vulnerable to adversarial attacks. Several previous works have demonstrated the feasibility of adversarial attacks on traffic sign recognition models. Traffic signs are particularly promising for adversarial attack research due to the ease of performing real-world attacks using printed signs or stickers. In this work, we survey existing works performing either digital or real-world attacks on traffic sign detection and classification models. We provide an overview of the latest advancements and highlight the existing research areas that require further investigation.
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MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents
MAGIC uses three collaborating LLM agents to generate scene-aware adversarial patches and place them in real-world images, achieving higher attack success against YOLO and DETR detectors than the natural diffusion attack baseline.