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.

翻译：深度神经网络安全性持续受到关注，可见光物理攻击已有大量研究，但红外领域的探索相对有限。现有方法（如采用灯泡板和QR服的白盒红外攻击）缺乏真实性与隐蔽性，而基于冷热补丁的黑盒方法往往难以保证鲁棒性。为弥合这些差距，我们提出对抗红外曲线（Adversarial Infrared Curves, AdvIC）。该方法利用粒子群优化算法优化两条贝塞尔曲线，并在物理世界中采用冷补丁引入扰动，生成用于物理样本制作的红外曲线图案。大量实验验证了AdvIC的有效性：数字攻击与物理攻击的成功率分别达94.8%和67.2%。通过对比分析证明了其隐蔽性，鲁棒性评估表明AdvIC优于基准方法。当针对多种先进检测器部署时，AdvIC达到76.8%的平均攻击成功率，彰显其强鲁棒性。我们进一步探索了针对AdvIC的对抗防御策略，并评估了不同防御机制对其影响。鉴于AdvIC对现实世界视觉应用系统具有重大安全隐患，亟需引起关注并采取缓解措施。