  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.
