  Watermarking is a tool for actively identifying and attributing the images
generated by latent diffusion models. Existing methods face the dilemma of
image quality and watermark robustness. Watermarks with superior image quality
usually have inferior robustness against attacks such as blurring and JPEG
compression, while watermarks with superior robustness usually significantly
damage image quality. This dilemma stems from the traditional paradigm where
watermarks are injected and detected in pixel space, relying on pixel
perturbation for watermark detection and resilience against attacks. In this
paper, we highlight that an effective solution to the problem is to both inject
and detect watermarks in the latent diffusion space, and propose Latent
Watermark with a progressive training strategy. It weakens the direct
connection between quality and robustness and thus alleviates their
contradiction. We conduct evaluations on two datasets and against 10 watermark
attacks. 6 metrics measure the image quality and watermark robustness. Results
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. Our code will be available
at https://github.com/RichardSunnyMeng/LatentWatermark.
