Protecting the Intellectual Property of Diffusion Models by the Watermark Diffusion Process. (arXiv:2306.03436v1 [cs.CR])

Diffusion models have emerged as state-of-the-art deep generative
architectures with the increasing demands for generation tasks. Training large
diffusion models for good performance requires high resource costs, making them
valuable intellectual properties to protect. While most of the existing
ownership solutions, including watermarking, mainly focus on discriminative
models. This paper proposes WDM, a novel watermarking method for diffusion
models, including watermark embedding, extraction, and verification. WDM embeds
the watermark data through training or fine-tuning the diffusion model to learn
a …

art demands diffusion models focus generative high intellectual property large making ownership performance process protect protecting solutions state training watermarking