Dynamic Physical Adversarial LED Patterns via Reinforcement Learning

Physical adversarial attacks expose important limitations of modern vision systems, but most existing methods rely on static patches or textures. We introduce a new problem setting: closed-loop physical adversarial control, where an agent interacts with a real-world perception system via a physical channel and receives only detector-level feedback. We optimize LED patterns with PR-SAC in a high-dimensional continuous action space. We show that latent action parameterization with deterministic upsampling improves training stability compared to direct control of individual LEDs. Across YOLOv8, Faster R-CNN, and RetinaNet, the learned patterns reduce person-detection confidence, decrease mAP@0.5, and exhibit partial cross-model transfer. We further analyze robustness under distance and viewpoint changes, interaction with a defense mechanism, and failure modes such as Q-drift, entropy collapse, and degradation after initial improvement. Our results suggest that dynamic physical attacks should be evaluated not only by best-case attack strength, but also by stability, transferability, and robustness under real-world variation.