CtrlCamo: Controllable Adversarial Camouflage Editing for Vehicle Detection

Deep neural networks (DNNs) have achieved remarkable success in computer vision but remain vulnerable to adversarial attacks. For vehicle detection, many existing attacks rely on imperceptible perturbations or localized adversarial patches, which can limit attack effectiveness or introduce visually conspicuous patterns. In this paper, we introduce $\text{CtrlCamo}$, a controllable framework for adversarial camouflage editing, which edits vehicle appearance to reduce detector performance while maintaining visual consistency with the environment. CtrlCamo supports two stylization strategies: image-level, which adapts vehicles to their surroundings, and scene-level, which aligns them with semantic concepts in the scene. A two-stage ControlNet fine-tuning pipeline jointly enforces structural preservation, style consistency, and adversarial effectiveness. Experiments on COCO and LINZ demonstrate at least 38% ${\mathrm{AP}}_{50}$ reduction over prior methods while improving structural preservation and perceptual consistency. The resulting adversarial effects also transfer to unseen black-box detectors. Code and data will be released upon paper acceptance to facilitate further research.