  Deep neural network security is a persistent concern, with considerable
research on visible light physical attacks but limited exploration in the
infrared domain. Existing approaches, like white-box infrared attacks using
bulb boards and QR suits, lack realism and stealthiness. Meanwhile, black-box
methods with cold and hot patches often struggle to ensure robustness. To
bridge these gaps, we propose Adversarial Infrared Curves (AdvIC). Using
Particle Swarm Optimization, we optimize two Bezier curves and employ cold
patches in the physical realm to introduce perturbations, creating infrared
curve patterns for physical sample generation. Our extensive experiments
confirm AdvIC's effectiveness, achieving 94.8\% and 67.2\% attack success rates
for digital and physical attacks, respectively. Stealthiness is demonstrated
through a comparative analysis, and robustness assessments reveal AdvIC's
superiority over baseline methods. When deployed against diverse advanced
detectors, AdvIC achieves an average attack success rate of 76.8\%, emphasizing
its robust nature. we explore adversarial defense strategies against AdvIC and
examine its impact under various defense mechanisms. Given AdvIC's substantial
security implications for real-world vision-based applications, urgent
attention and mitigation efforts are warranted.
