Physical Strip Attack for Object Detection in Optical Remote Sensing

Changfeng Sun, Jingwei Sun, Xuchong Zhang, Yitong Li, Qicheng Bai, Hongbin Sun

Abstract: A growing trend in the field of adversarial attacks is evolving from the digital domain to the more challenging physical domain. The previous works mainly employ printable adversarial patches with special textures in real-world physical attacks. However, due to lighting conditions and atmospheric scattering, the texture-based patches are prone to distortion in the long-range situation than in the close-range case, resulting in poor physical attack performance in remote sensing scenarios. Therefore, this article proposes a new physical attack method using single-color strip-based patches to hide the objects from being detected correctly in optical aerial detection. Specifically, we design a differentiable representation and an optimization method to optimize the position, thickness, and color of the adversarial strips. Compared with the traditional complex texture-based patch, the proposed strip-based patch is more robust when mapping from the digital domain to the physical domain. Extensive experiments are conducted on multiple datasets and real-world scenarios to evaluate the attack performance of various attack methods. The results show that the proposed strip-based adversarial patch has better attack performance against white-box, black-box, and even defense detectors. Furthermore, we can improve the physical attack success rate (ASR) in remote sensing scenarios by about 70% compared with previous texture-based methods.