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.

深度神经网络安全是一个持久关注的问题，现有研究主要关注可见光物理攻击，对红外领域的探索有限。针对这些差距，我们提出了 Adversarial Infrared Curves (AdvIC)。通过使用粒子群优化，我们优化了两个贝塞尔曲线，并在物理领域中使用冷贴片引入干扰，创造了用于实际样本生成的红外曲线模式。我们的广泛实验证实了 AdvIC 的有效性，分别在数字和物理攻击中实现了 94.8％和 67.2％的攻击成功率。通过比较分析证明了其隐蔽性，而鲁棒性评估显示 AdvIC 优于基准方法。在对不同的高级检测器进行部署时，AdvIC 实现了平均攻击成功率 76.8％，凸显了其强大的特性。我们还探讨了针对 AdvIC 的对抗性防御策略，并对其在各种防御机制下的影响进行了检查。考虑到 AdvIC 对现实世界基于视觉的应用的重要安全意义，亟需关注和缓解。