DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization
This addresses copyright protection for artists and content creators against plagiarism in AI-generated art, representing a novel application in a specific domain.
The paper tackles the problem of copyright infringement in Stable Diffusion customization by proposing DUAW, an invisible data-free universal adversarial watermark that disrupts model outputs, with experiments showing it effectively distorts generated images to be discernible by humans and a classifier.
Stable Diffusion (SD) customization approaches enable users to personalize SD model outputs, greatly enhancing the flexibility and diversity of AI art. However, they also allow individuals to plagiarize specific styles or subjects from copyrighted images, which raises significant concerns about potential copyright infringement. To address this issue, we propose an invisible data-free universal adversarial watermark (DUAW), aiming to protect a myriad of copyrighted images from different customization approaches across various versions of SD models. First, DUAW is designed to disrupt the variational autoencoder during SD customization. Second, DUAW operates in a data-free context, where it is trained on synthetic images produced by a Large Language Model (LLM) and a pretrained SD model. This approach circumvents the necessity of directly handling copyrighted images, thereby preserving their confidentiality. Once crafted, DUAW can be imperceptibly integrated into massive copyrighted images, serving as a protective measure by inducing significant distortions in the images generated by customized SD models. Experimental results demonstrate that DUAW can effectively distort the outputs of fine-tuned SD models, rendering them discernible to both human observers and a simple classifier.