Deep neural networks have achieved remarkable success in computer vision but remain highly vulnerable to adversarial attacks. This paper proposes a new framework that formulates vehicle camouflage attacks as a conditional image-editing problem, exploring both image-level and scene-level camouflage generation strategies. The method fine-tunes a ControlNet to synthesize camouflaged vehicles directly on real images while enforcing vehicle structural fidelity, style consistency, and adversarial effectiveness through a unified objective. Experiments on COCO and LINZ datasets demonstrate that the approach achieves significantly stronger attack effectiveness with more than 38% AP50 decrease, while better preserving vehicle structure and improving human-perceived stealthiness compared to existing methods.