Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. The ideal defense should be effective, efficient, easy to deploy, and capable of withstanding adaptive attacks. In this paper, we adopt a counterattack strategy to propose a novel and general methodology for defending adversarial attacks. Two types of defensive patches, canary and woodpecker, are specially-crafted and injected into the model input to proactively probe or counteract potential adversarial patches. In this manner, adversarial patch attacks can be effectively detected by simply analyzing the model output, without the need to alter the target model. Moreover, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments.

翻译：目标检测已在各类任务中得到广泛应用，但其同样易受对抗性补丁攻击。理想的防御手段应具备高效性、低开销、易于部署并能抵御自适应攻击。本文采用反制策略，提出一种新颖且通用的对抗攻击防御方法。我们专门设计并注入了两种防御性补丁——"金丝雀"与"啄木鸟"，通过主动探测或抵消潜在的对抗性补丁来增强模型输入。通过这种方式，仅需分析模型输出即可有效检测对抗性补丁攻击，而无需修改目标模型。此外，我们采用随机化的金丝雀与啄木鸟注入模式以防御针对防御机制的攻击。综合实验验证了所提方法的有效性与实用性。结果表明，即使在面对未知攻击方法时，金丝雀与啄木鸟仍能实现高性能检测，且仅产生有限的时间开销。自适应攻击实验进一步证明，本方法对防御感知型攻击亦展现出足够的鲁棒性。