The escalating threat of backdoor risks in deep vision models is a pressing concern. Existing research on backdoor attacks is often confined to a single modality, neglecting the challenges posed by multi-modality scene perception. This work is a pioneer of backdoor attacks in RGB-Thermal (RGB-T) semantic segmentation. We overcome the critical limitation of current segmentation backdoor attacks that indiscriminately compromise all objects of a victim class, failing to provide fine-grained control for selectively targeting specific objects as required by adversaries. To address this, we introduce a novel Object-level Backdoor Attack pipeline, termed OBA. The OBA first employs a precise data poisoning (PDP) to lock a specific victim object. Specifically, the PDP embeds the trigger into the only victim object and modifies its label’s pixels at the corresponding positions, thus enabling object-level attacks. In addition, the domain gap between static single-modality triggers and multi-modality scenarios limits the PDP. We therefore introduce a Cross-Modality Trigger Generation (CMTG) method. Through style designs of triggers and cross-modality trigger co-optimization, the target domain semantics and multi-modality model perception patterns are encoded into triggers, achieving high effectiveness, stealth, and physical feasibility of triggers. Extensive experiments show that the proposed OBA enables precise manipulation of the designated object within the specific class.

The advancement of deep object detectors has greatly affected safety-critical fields like autonomous driving. However, physical adversarial camouflage poses a significant security risk by altering object textures to deceive detectors. Existing techniques struggle with variable physical environments, facing two main challenges: 1) inconsistent sampling point densities across distances hinder the gradient optimization from ensuring local continuity, and 2) updating texture gradients from multiple angles causes conflicts, reducing optimization stability and attack effectiveness. To address these issues, we propose a novel adversarial camouflage framework based on gradient optimization. First, we introduce a gradient calibration strategy, which ensures consistent gradient updates across distances by propagating gradients from sparsely to unsampled texture points, thereby expanding the attack's effective range. Additionally, we develop a gradient decorrelation method, which prioritizes and orthogonalizes gradients based on loss values, enhancing stability and effectiveness in multi-angle optimization by eliminating redundant or conflicting updates. Extensive experimental results on various detection models, angles, and distances show that our method significantly surpasses the state-of-the-art, with an average attack success rate (ASR) increase of 13.46\% across distances and 11.03\% across angles. Furthermore, experiments in real-world settings confirm the method's threat potential, highlighting the urgent need for more robust autopilot systems less prone to spoofing.