Physical adversarial patches printed on clothing can easily allow individuals to evade person detectors. However, most existing adversarial patch generation methods prioritize attack effectiveness over stealthiness, resulting in patches that are aesthetically unpleasing. Although existing methods using generative adversarial networks or diffusion models can produce more natural-looking patches, they often struggle to balance stealthiness with attack effectiveness and lack flexibility for user customization. To address these challenges, we propose a novel diffusion-based customizable patch generation framework termed DiffPatch, specifically tailored for creating naturalistic and customizable adversarial patches. Our approach enables users to utilize a reference image as the source, rather than starting from random noise, and incorporates masks to craft naturalistic patches of various shapes, not limited to squares. To prevent the original semantics from being lost during the diffusion process, we employ Null-text inversion to map random noise samples to a single input image and generate patches through Incomplete Diffusion Optimization (IDO). Notably, while maintaining a natural appearance, our method achieves a comparable attack performance to state-of-the-art non-naturalistic patches when using similarly sized attacks. Using DiffPatch, we have created a physical adversarial T-shirt dataset, AdvPatch-1K, specifically targeting YOLOv5s. This dataset includes over a thousand images across diverse scenarios, validating the effectiveness of our attack in real-world environments. Moreover, it provides a valuable resource for future research.

本研究解决了现有对抗性贴片生成方法在隐蔽性与攻击效果之间难以平衡的问题。我们提出了一种新的扩散模型框架DiffPatch，使用户能够利用参考图像生成自然且可定制的对抗性贴片，并采用Null-text反演技术保持原始语义。实验结果表明，DiffPatch生成的贴片在视觉上无明显不适，且在攻击效果上与最先进的贴片相当。

DiffPatch：使用扩散模型生成可定制的对抗性贴片