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Stable Diffusion (SD) customization approaches enable users to personalize SDmodel outputs, greatly enhancing the flexibility and diversity of AI art.However, they also allow individuals to plagiarize specific styles or subjectsfrom copyrighted images, which raises significant concerns about potentialcopyright infringement. To address this issue, we propose an invisibledata-free universal adversarial watermark (DUAW), aiming to protect a myriad ofcopyrighted images from different customization approaches across variousversions of SD models. First, DUAW is designed to disrupt the variationalautoencoder during SD customization. Second, DUAW operates in a data-freecontext, where it is trained on synthetic images produced by a Large LanguageModel (LLM) and a pretrained SD model. This approach circumvents the necessityof directly handling copyrighted images, thereby preserving theirconfidentiality. Once crafted, DUAW can be imperceptibly integrated intomassive copyrighted images, serving as a protective measure by inducingsignificant distortions in the images generated by customized SD models.Experimental results demonstrate that DUAW can effectively distort the outputsof fine-tuned SD models, rendering them discernible to both human observers anda simple classifier.

DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization