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The advancement in text-to-image models has led to astonishing artisticperformances. However, several studios and websites illegally fine-tune thesemodels using artists' artworks to mimic their styles for profit, which violatesthe copyrights of artists and diminishes their motivation to produce originalworks. Currently, there is a notable lack of research focusing on this issue.In this paper, we propose a novel watermarking framework that detects mimicryin text-to-image models through fine-tuning. This framework embeds subtlewatermarks into digital artworks to protect their copyrights while stillpreserving the artist's visual expression. If someone takes watermarkedartworks as training data to mimic an artist's style, these watermarks canserve as detectable indicators. By analyzing the distribution of thesewatermarks in a series of generated images, acts of fine-tuning mimicry usingstolen victim data will be exposed. In various fine-tune scenarios and againstwatermark attack methods, our research confirms that analyzing the distributionof watermarks in artificially generated images reliably detects unauthorizedmimicry.

Steal My Artworks for Fine-tuning? A Watermarking Framework for Detecting Art Theft Mimicry in Text-to-Image Models