Fight Fire with Fire: Combating Adversarial Patch Attacks using
  Pattern-randomized Defensive Patches
Jianan Feng Jiachun Li Changqing Miao Jianjun Huang Wei You Wenchang Shi Bin Liang
Object detection has found extensive applications in various tasks, but it is
also susceptible to adversarial patch attacks. Existing defense methods often
necessitate modifications to the target model or result in unacceptable time
overhead. In this paper, we adopt a counterattack approach, following the
principle of "fight fire with fire," and propose a novel and general
methodology for defending adversarial attacks. We utilize an active defense
strategy by injecting two types of defensive patches, canary and woodpecker,
into the input to proactively probe or weaken potential adversarial patches
without altering the target model. Moreover, inspired by randomization
techniques employed in software security, 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.