DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion
  Customization
Xiaoyu Ye Hao Huang Jiaqi An Yongtao Wang
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.