Prior works on physical adversarial camouflage against vehicle detectors mainly focus on the effectiveness and robustness of the attack. The current most successful methods optimize 3D vehicle texture at a pixel level. However, this results in conspicuous and attention-grabbing patterns in the generated camouflage, which humans can easily identify. To address this issue, we propose a Customizable and Natural Camouflage Attack (CNCA) method by leveraging an off-the-shelf pre-trained diffusion model. By sampling the optimal texture image from the diffusion model with a user-specific text prompt, our method can generate natural and customizable adversarial camouflage while maintaining high attack performance. With extensive experiments on the digital and physical worlds and user studies, the results demonstrate that our proposed method can generate significantly more natural-looking camouflage than the state-of-the-art baselines while achieving competitive attack performance. Our code is available at \href{https://anonymous.4open.science/r/CNCA-1D54}{https://anonymous.4open.science/r/CNCA-1D54}

翻译：先前针对车辆检测器的物理对抗伪装研究主要关注攻击的有效性与鲁棒性。当前最成功的方法在像素级别优化三维车辆纹理，但这导致生成的伪装图案过于显眼且引人注目，人类可轻易识别。为解决此问题，我们提出一种基于预训练扩散模型的可定制化自然伪装攻击方法。通过使用用户特定文本提示从扩散模型中采样最优纹理图像，本方法能在保持高攻击性能的同时生成自然且可定制的对抗伪装。通过在数字与物理场景的广泛实验及用户研究，结果表明本方法生成的伪装视觉自然度显著优于现有基线方法，同时达到具有竞争力的攻击性能。代码发布于 \href{https://anonymous.4open.science/r/CNCA-1D54}{https://anonymous.4open.science/r/CNCA-1D54}