AngleRoCL: Angle-Robust Concept Learning for Physically View-Invariant T2I Adversarial Patches
Journal:
arXiv
Published Date:
Jun 11, 2025
Abstract
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 T2I patches' attack effectiveness
when observed from different views in the physical world (i.e., angle
robustness of the T2I adversarial patches). In this paper, we study the angle
robustness of T2I adversarial patches comprehensively, revealing their
angle-robust issues, demonstrating that texts affect the angle robustness of
generated patches significantly, and task-specific linguistic instructions fail
to enhance the angle robustness. Motivated by the studies, we introduce
Angle-Robust Concept Learning (AngleRoCL), a simple and flexible approach that
learns a generalizable concept (i.e., text embeddings in implementation)
representing the capability of generating angle-robust patches. The learned
concept can be incorporated into textual prompts and guides T2I models to
generate patches with their attack effectiveness inherently resistant to
viewpoint variations. Through extensive simulation and physical-world
experiments on five SOTA detectors across multiple views, we demonstrate that
AngleRoCL significantly enhances the angle robustness of T2I adversarial
patches compared to baseline methods. Our patches maintain high attack success
rates even under challenging viewing conditions, with over 50% average relative
improvement 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.
