Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Space
Zheling Meng Bo Peng Jing Dong
Watermarking is a tool for actively identifying and attributing the images
generated by latent diffusion models. Existing methods face the dilemma of
watermark robustness and image quality. The reason for this dilemma is that
watermark detection is performed in pixel space, implying an intrinsic link
between image quality and watermark robustness. In this paper, we highlight
that an effective solution to the problem is to both inject and detect
watermarks in latent space, and propose Latent Watermark (LW) with a
progressive training strategy. Experiments show that compared to the recently
proposed methods such as StegaStamp, StableSignature, RoSteALS and TreeRing, LW
not only surpasses them in terms of robustness but also offers superior image
quality. When we inject 64-bit messages, LW can achieve an identification
performance close to 100% and an attribution performance above 97% under 9
single-attack scenarios and one all-attack scenario. Our code will be available
on GitHub.