Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models
Journal:
arXiv
Published Date:
Apr 21, 2025
Abstract
Ethical concerns surrounding copyright protection and inappropriate content
generation pose challenges for the practical implementation of diffusion
models. One effective solution involves watermarking the generated images.
Existing methods primarily focus on ensuring that watermark embedding does not
degrade the model performance. However, they often overlook critical challenges
in real-world deployment scenarios, such as the complexity of watermark key
management, user-defined generation parameters, and the difficulty of
verification by arbitrary third parties. To address this issue, we propose
Gaussian Shading++, a diffusion model watermarking method tailored for
real-world deployment. We propose a double-channel design that leverages
pseudorandom error-correcting codes to encode the random seed required for
watermark pseudorandomization, achieving performance-lossless watermarking
under a fixed watermark key and overcoming key management challenges.
Additionally, we model the distortions introduced during generation and
inversion as an additive white Gaussian noise channel and employ a novel soft
decision decoding strategy during extraction, ensuring strong robustness even
when generation parameters vary. To enable third-party verification, we
incorporate public key signatures, which provide a certain level of resistance
against forgery attacks even when model inversion capabilities are fully
disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only
maintains performance losslessness but also outperforms existing methods in
terms of robustness, making it a more practical solution for real-world
deployment.
