The ethical need to protect AI-generated content has been a significant concern in recent years. While existing watermarking strategies have demonstrated success in detecting synthetic content (detection), there has been limited exploration in identifying the users responsible for generating these outputs from a single model (owner identification). In this paper, we focus on both practical scenarios and propose a unified watermarking framework for content copyright protection within the context of diffusion models. Specifically, we consider two parties: the model provider, who grants public access to a diffusion model via an API, and the users, who can solely query the model API and generate images in a black-box manner. Our task is to embed hidden information into the generated contents, which facilitates further detection and owner identification. To tackle this challenge, we propose a Watermark-conditioned Diffusion model called WaDiff, which manipulates the watermark as a conditioned input and incorporates fingerprinting into the generation process. All the generative outputs from our WaDiff carry user-specific information, which can be recovered by an image extractor and further facilitate forensic identification. Extensive experiments are conducted on two popular diffusion models, and we demonstrate that our method is effective and robust in both the detection and owner identification tasks. Meanwhile, our watermarking framework only exerts a negligible impact on the original generation and is more stealthy and efficient in comparison to existing watermarking strategies.

翻译：近年来，人工智能生成内容的伦理保护需求已成为重要议题。现有水印策略在检测合成内容方面已取得显著成效，但针对从单一模型中识别生成输出内容的用户身份（所有者识别）的研究仍较为有限。本文聚焦于这两种实际场景，提出了一种面向扩散模型内容版权保护的统一水印框架。具体而言，我们考虑两方参与者：模型提供方（通过API向公众开放扩散模型访问权限）与用户（仅能以黑盒方式查询模型API并生成图像）。我们的目标是在生成内容中嵌入隐藏信息，以同时支持后续的检测与所有者识别任务。针对这一挑战，我们提出了一种名为WaDiff的水印条件扩散模型，该模型将水印作为条件输入，在生成过程中融入指纹特征。WaDiff的所有生成输出均携带用户特定信息，这些信息可通过图像提取器恢复，进而支持取证识别。我们在两种主流扩散模型上开展了大量实验，结果表明本方法在检测与所有者识别任务中均具有有效性与鲁棒性。同时，本水印框架对原始生成质量的影响可忽略不计，且相比现有水印策略更具隐蔽性与高效性。