AI RESEARCH
In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing
arXiv CS.CV
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ArXi:2603.19456v1 Announce Type: new Deep neural networks (DNNs) have achieved remarkable success in computer vision but remain highly vulnerable to adversarial attacks. Among them, camouflage attacks manipulate an object's visible appearance to deceive detectors while remaining stealthy to humans. In this paper, we propose a new framework that formulates vehicle camouflage attacks as a conditional image-editing problem.