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.

通过使用不可见的无数据普适对抗水印（DUAW），本研究提出了一种保护众多版权图像免受不同自定义方法和不同版本的 Stable Diffusion (SD) 模型侵权的方法。DUAW 的设计目的是干扰 Stable Diffusion 定制过程中的变分自编码器，它在一个无数据的环境中操作，通过对由大型语言模型（LLM）和预训练的 SD 模型产生的合成图像进行训练。该方法规避了直接处理受版权保护图像的必要性，从而保护了其机密性。实验证明，DUAW 能够有效地扭曲精细调整的 SD 模型的输出，使其对人类观察者和简单分类器都可分辨出来。