Infrared Thermal-Guided Adversarial Patch Defense for Robust Visible Person Detectors

Adversarial patch attacks on visible person detectors pose significant risks to recognition and safety. To counter this, we propose a defense method that integrates thermal imaging with RGB images to exploit differences in thermal characteristics between adversarial patches and humans. Our approach segments thermal images using selective search to generate mask sets and apply them to RGB images for attack detection and mitigation. To evaluate the proposed method, we created a physical attack dataset by conducting a hiding attack using printed adversarial patches against a person detector. This dataset enabled a quantitative assessment of our method. Experimental results demonstrate that our approach effectively defends against adversarial patches while maintaining performance on clean images, validating its robustness in practical scenarios.