X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection. (arXiv:2302.09491v1 [cs.CR])

Adversarial attacks are valuable for evaluating the robustness of deep
learning models. Existing attacks are primarily conducted on the visible light
spectrum (e.g., pixel-wise texture perturbation). However, attacks targeting
texture-free X-ray images remain underexplored, despite the widespread
application of X-ray imaging in safety-critical scenarios such as the X-ray
detection of prohibited items. In this paper, we take the first step toward the
study of adversarial attacks targeted at X-ray prohibited item detection, and
reveal the serious threats posed by such …

adv adversarial adversarial attacks application attacks critical deep learning detection free images object physical pixel robustness safety safety-critical spectrum study targeting visible