Multi-Task Robustness Enhancement Framework against Various Adversarial Patches

Autonomous systems leveraging visual perception face a rising threat from adversarial patches, jeopardizing their robustness. Existing defense methods adaptable to various pre-trained models typically rely on observed patch characteristics or prior attack data, having difficulty adapting to new threats. This study innovatively focuses on modeling patch attack behavior instead of existing patches, proposing a unified robustness enhancement framework against various adversarial patches. Through self-supervised learning, we accurately locate diverse adversarial patches without prior attack knowledge. Furthermore, we introduce an efficient adaptive patch inpainting method to mitigate patch impact while maintaining visual coherence. Experiments show that our methods effectively boost the robustness of visual perception models against various adversarial patches across different tasks.