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

Computer Science > Computer Vision and Pattern Recognition [Submitted on 27 Mar 2026] Gaussian Shannon: High-Precision Diffusion Model Watermarking Based on Communication Yi Zhang, Hongbo Huang, Liang-Jie Zhang 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: this https URL Comments: Accepted by CVPR 2026 Findings Subjects: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR) Cite as: arXiv:2603.26167 [cs.CV]   (or arXiv:2603.26167v1 [cs.CV] for this version)   https://doi.org/10.48550/arXiv.2603.26167 Focus to learn more Submission history From: Hongbo Huang [view email] [v1] Fri, 27 Mar 2026 08:34:53 UTC (3,105 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CV < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)