Adversarial Infrared Geometry: Using Geometry to Perform Adversarial
  Attack against Infrared Pedestrian Detectors
Kalibinuer Tiliwalidi
Currently, infrared imaging technology enjoys widespread usage, with infrared
object detection technology experiencing a surge in prominence. While previous
studies have delved into physical attacks on infrared object detectors, the
implementation of these techniques remains complex. For instance, some
approaches entail the use of bulb boards or infrared QR suits as perturbations
to execute attacks, which entail costly optimization and cumbersome deployment
processes. Other methodologies involve the utilization of irregular aerogel as
physical perturbations for infrared attacks, albeit at the expense of
optimization expenses and perceptibility issues. In this study, we propose a
novel infrared physical attack termed Adversarial Infrared Geometry
(\textbf{AdvIG}), which facilitates efficient black-box query attacks by
modeling diverse geometric shapes (lines, triangles, ellipses) and optimizing
their physical parameters using Particle Swarm Optimization (PSO). Extensive
experiments are conducted to evaluate the effectiveness, stealthiness, and
robustness of AdvIG. In digital attack experiments, line, triangle, and ellipse
patterns achieve attack success rates of 93.1\%, 86.8\%, and 100.0\%,
respectively, with average query times of 71.7, 113.1, and 2.57, respectively,
thereby confirming the efficiency of AdvIG. Physical attack experiments are
conducted to assess the attack success rate of AdvIG at different distances. On
average, the line, triangle, and ellipse achieve attack success rates of
61.1\%, 61.2\%, and 96.2\%, respectively. Further experiments are conducted to
comprehensively analyze AdvIG, including ablation experiments, transfer attack
experiments, and adversarial defense mechanisms. Given the superior performance
of our method as a simple and efficient black-box adversarial attack in both
digital and physical environments, we advocate for widespread attention to
AdvIG.