Gaussian Shannon: High-Precision Diffusion Model Watermarking Based on Communication

• URL: http://arxiv.org/abs/2603.26167v1
• Date: Fri, 27 Mar 2026 08:34:53 GMT
• Title: Gaussian Shannon: High-Precision Diffusion Model Watermarking Based on Communication
• Abstract summary: Diffusion models generate high-quality images but pose serious risks like copyright violation and disinformation.<n>We propose a watermarking framework that treats the diffusion process as a noisy communication channel.<n>Our method embeds watermarks in the initial Gaussian noise without fine-tuning or quality loss.
• Score: 4.347817782863135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models generate high-quality images but pose serious risks like copyright violation and disinformation. Watermarking is a key defense for tracing and authenticating AI-generated content. However, existing methods rely on threshold-based detection, which only supports fuzzy matching and cannot recover structured watermark data bit-exactly, making them unsuitable for offline verification or applications requiring lossless metadata (e.g., licensing instructions). To address this problem, in this paper, we propose Gaussian Shannon, a watermarking framework that treats the diffusion process as a noisy communication channel and enables both robust tracing and exact bit recovery. Our method embeds watermarks in the initial Gaussian noise without fine-tuning or quality loss. We identify two types of channel interference, namely local bit flips and global stochastic distortions, and design a cascaded defense combining error-correcting codes and majority voting. This ensures reliable end-to-end transmission of semantic payloads. Experiments across three Stable Diffusion variants and seven perturbation types show that Gaussian Shannon achieves state-of-the-art bit-level accuracy while maintaining a high true positive rate, enabling trustworthy rights attribution in real-world deployment. The source code have been made available at: https://github.com/Rambo-Yi/Gaussian-Shannon

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