[2603.00194] SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00194 (cs) [Submitted on 27 Feb 2026] Title:SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models Authors:Yang Yang, Xinze Zou, Zehua Ma, Han Fang, Weiming Zhang View a PDF of the paper titled SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models, by Yang Yang and 3 other authors View PDF HTML (experimental) Abstract:The rise of text-to-video generation models has raised growing concerns over content authenticity, copyright protection, and malicious misuse. Watermarking serves as an effective mechanism for regulating such AI-generated content, where high fidelity and strong robustness are particularly critical. Recent generative image watermarking methods provide a promising foundation by leveraging watermark information and pseudo-random keys to control the initial sampling noise, enabling lossless embedding. However, directly extending these techniques to videos introduces two key limitations: Existing designs implicitly rely on strict alignment between video frames and frame-dependent pseudo-random binary sequences used for watermark encryption. Once this alignment is disrupted, subsequent watermark extraction becomes unreliable; and Video-specific distortions, such as inter-frame compression, significantly degrade watermark reliability. To address these issues, we propose SKeDA, a generative watermarking framework tailored for text-to-video diffusion mo...