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

• URL: http://arxiv.org/abs/2603.00194v1
• Date: Fri, 27 Feb 2026 06:18:03 GMT
• Title: SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models
• Authors: Yang Yang, Xinze Zou, Zehua Ma, Han Fang, Weiming Zhang,
• Abstract summary: We propose a generative watermarking framework tailored for text-to-video diffusion models.<n> SKeDA consists of two components: (1) Shuffle-Key-based Distribution-preserving Sampling (SKe) employs a single base pseudo-random binary sequence for watermark encryption and derives frame-level encryption sequences through permutation.<n>Extensive experiments demonstrate that SKeDA preserves high video generation quality and watermark robustness.
• Score: 40.540302276054376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 models. SKeDA consists of two components: (1) Shuffle-Key-based Distribution-preserving Sampling (SKe) employs a single base pseudo-random binary sequence for watermark encryption and derives frame-level encryption sequences through permutation. This design transforms watermark extraction from synchronization-sensitive sequence decoding into permutation-tolerant set-level aggregation, substantially improving robustness against frame reordering and loss; and (2) Differential Attention (DA), which computes inter-frame differences and dynamically adjusts attention weights during extraction, enhancing robustness against temporal distortions. Extensive experiments demonstrate that SKeDA preserves high video generation quality and watermark robustness.

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