A Consistency and Integration Model with Adaptive Thresholds for Weakly Supervised Object Localization

Abstract

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.