  While extensive research exists on physical adversarial attacks within the
visible spectrum, studies on such techniques in the infrared spectrum are
limited. Infrared object detectors are vital in modern technological
applications but are susceptible to adversarial attacks, posing significant
security threats. Previous studies using physical perturbations like light bulb
arrays and aerogels for white-box attacks, or hot and cold patches for
black-box attacks, have proven impractical or limited in multi-view support. To
address these issues, we propose the Adversarial Infrared Grid (AdvGrid), which
models perturbations in a grid format and uses a genetic algorithm for
black-box optimization. These perturbations are cyclically applied to various
parts of a pedestrian's clothing to facilitate multi-view black-box physical
attacks on infrared pedestrian detectors. Extensive experiments validate
AdvGrid's effectiveness, stealthiness, and robustness. The method achieves
attack success rates of 80.00\% in digital environments and 91.86\% in physical
environments, outperforming baseline methods. Additionally, the average attack
success rate exceeds 50\% against mainstream detectors, demonstrating AdvGrid's
robustness. Our analyses include ablation studies, transfer attacks, and
adversarial defenses, confirming the method's superiority.
