In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing
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. Specifically, we explore both image-level and scene-level camouflage generation strategies, and fine-tune a ControlNet to synthesize camouflaged vehicles directly on real images. We design a unified objective that jointly enforces vehicle structural fidelity, style consistency, and adversarial effectiveness. Extensive experiments on the COCO and LINZ datasets show that our method achieves significantly stronger attack effectiveness, leading to more than 38% AP50 decrease, while better preserving vehicle structure and improving human-perceived stealthiness compared to existing approaches. Furthermore, our framework generalizes effectively to unseen black-box detectors and exhibits promising transferability to the physical world. Project page is available at https://humansensinglab.github.io/CtrlCamo
69c18987e9374d7b3df94e36
Xiao Fang
claimed_verified
2026-03-24T09:22:34.383Z
6697d30fbfec78c1bf6a4962
/avatars/17cfd7d7eb2b89b5051bd912e41e5a62.svg
Xiao Fang
xiaofanghf
user
xiaofanghf
69c18987e9374d7b3df94e37
Yiming Gong
69c18987e9374d7b3df94e38
Stanislav Panev
69c18987e9374d7b3df94e39
Celso de Melo
69c18987e9374d7b3df94e3a
Shuowen Hu
69c18987e9374d7b3df94e3b
Shayok Chakraborty
69c18987e9374d7b3df94e3c
Fernando De la Torre
In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing
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. Specifically, we explore both image-level and scene-level camouflage generation strategies, and fine-tune a ControlNet to synthesize camouflaged vehicles directly on real images. We design a unified objective that jointly enforces vehicle structural fidelity, style consistency, and adversarial effectiveness. Extensive experiments on the COCO and LINZ datasets show that our method achieves significantly stronger attack effectiveness, leading to more than 38% AP50 decrease, while better preserving vehicle structure and improving human-perceived stealthiness compared to existing approaches. Furthermore, our framework generalizes effectively to unseen black-box detectors and exhibits promising transferability to the physical world. Project page is available at https://humansensinglab.github.io/CtrlCamo
A novel framework formulates vehicle camouflage attacks as a conditional image-editing problem using ControlNet to generate stealthy adversarial examples with preserved structure and enhanced transferability.
ControlNet conditional image-editing camouflage attacks adversarial attacks vehicle structural fidelity style consistency adversarial effectiveness black-box detectors