The advancement in text-to-image models has led to astonishing artistic performances. However, several studios and websites illegally fine-tune these models using artists' artworks to mimic their styles for profit, which violates the copyrights of artists and diminishes their motivation to produce original works. Currently, there is a notable lack of research focusing on this issue. In this paper, we propose a novel watermarking framework that detects mimicry in text-to-image models through fine-tuning. This framework embeds subtle watermarks into digital artworks to protect their copyrights while still preserving the artist's visual expression. If someone takes watermarked artworks as training data to mimic an artist's style, these watermarks can serve as detectable indicators. By analyzing the distribution of these watermarks in a series of generated images, acts of fine-tuning mimicry using stolen victim data will be exposed. In various fine-tune scenarios and against watermark attack methods, our research confirms that analyzing the distribution of watermarks in artificially generated images reliably detects unauthorized mimicry.

翻译：文本到图像模型的进步带来了惊人的艺术表现力。然而，部分工作室和网站非法利用艺术家的作品对这些模型进行微调，以模仿其艺术风格牟利，这侵犯了艺术家的著作权，也削弱了他们创作原创作品的动力。目前，针对这一问题的研究明显不足。本文提出了一种新颖的水印框架，通过微调过程检测文本到图像模型中的仿冒行为。该框架在数字艺术作品中嵌入细微水印以保护其著作权，同时保留艺术家的视觉表达。若有人将被水印标记的艺术作品作为训练数据来模仿艺术家风格，这些水印便可作为可检测指标。通过分析一系列生成图像中水印的分布特征，即可揭露利用被盗受害者数据进行微调仿冒的行为。在多种微调场景及面对水印攻击方法时，我们的研究证实，分析人工生成图像中的水印分布能够可靠地检测未经授权的仿冒行为。