AngleRoCL: Angle-Robust Concept Learning for Physically View-Invariant Adversarial Patches
Wenjun Ji, Yuxiang Fu, Luyang Ying, Deng-Ping Fan, Yuyi Wang, Ming-Ming Cheng, Ivor Tsang, Qing Guo
Cutting-edge works have demonstrated that text-to-image (T2I) diffusion models can generate adversarial patches that mislead state-of-the-art object detectors in the physical world, revealing detectors' vulnerabilities and risks. However, these methods neglect the adversarial patches' attack effectiveness when observed from different views in the physical world (\ie, angle robustness of the adversarial patches).  In this paper, for the first time, we study the angle robustness of generated patches comprehensively, revealing the angle-robust issues of existing works and demonstrating that input texts affect the angle robustness of generated patches significantly.  Motivated by the studies, we introduce Angle-Robust Concept Learning (AngleRoCL), a novel approach that learns a generalizable concept (\ie, specialized text embeddings in implementation) representing the capability of generating angle-robust patches. The learned concept can be incorporated into text prompts and guides T2I models to generate patches with their attack effectiveness inherently resistant to viewpoint variations. Through extensive simulation and physical-world experiments across multiple observation views, we demonstrate that AngleRoCL significantly enhances the angle robustness of generated patches compared to baseline methods.  Our patches maintain high attack success rates even under challenging viewing conditions, with an average improvement of xxx in attack effectiveness across multiple angles.  This research advances the understanding of physically angle-robust patches and provides insights into the relationship between textual concepts and physical properties in T2I-generated contents.