Beyond Image-Wide Color Shifts: Scene-Compatible Semantic-Region Triggers for Backdoor Attacks

Image-wide color space backdoor attacks can be visually subtle, but applying a single transformation to semantically distinct regions may produce chromatic changes that conflict with object boundaries and local illumination. We argue that stealth depends not only on perturbation magnitude, but also on where the transformation is applied and how its strength adapts to local content. Guided by this insight, we propose a scene-compatible semantic backdoor framework that decouples semantic carrier selection from spatially adaptive perturbation control. A stability-aware selection strategy identifies complete regions whose appearance changes are plausible within the surrounding scene, while texture-adaptive modulation assigns bounded, input-dependent color shifts according to local visual tolerance. Across four dataset–architecture settings, our method consistently achieves attack success rates above 98% while preserving clean accuracy and low perceptual distortion. Evaluations under representative defenses further suggest that semantically grounded, sample-dependent triggers challenge assumptions based on fixed or spatially regular trigger patterns.