Exploring Full-View RGB-T Vehicle Adversarial Robustness in the Physical World

Multimodal RGB-Thermal (RGB-T) vehicle detectors are widely assumed to be robust owing to their complementary dual-modality design. However, their security under adversarial attacks remains insufficiently explored, particularly across the full viewpoint hemisphere. In this paper, we present the first full-angle physical adversarial attack against RGB-T vehicle detectors, covering azimuth angles of $0^{\circ }$–$360^{\circ }$ and elevation angles of $0^{\circ }$–$90^{\circ }$. We first conduct a comprehensive full-angle vulnerability analysis, revealing that existing attack methods suffer significant performance degradation under this challenging setting. To address the identified challenges of modality imbalance, angular imbalance, and digital-to-physical transfer gap, we propose three key techniques: (1) an adversarial shape-texture co-optimization (STCO) method that balances attack effectiveness across both modalities; (2) an angle-adaptive attack strategy (AAAS) that allocates more optimization effort to hard-to-attack viewpoints; and (3) a cross-modal foreground and background Expectation over Transformation (CFBE) method that enhances physical-world transferability. We further propose an angle-adaptive adversarial training method as a defense baseline. Extensive experiments in both digital and physical worlds, demonstrate the effectiveness of our attack against the state-of-the-art RGB-T detector and its strong transferability to unseen detectors.