# VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models

arXiv: https://arxiv.org/abs/2504.16359 · HTML: https://mlsanity.com/paper/2504.16359/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://mlsanity.com/paper/2311.04145/) (2023)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [A Survey of Text Watermarking in the Era of Large Language Models](https://mlsanity.com/paper/2312.07913/) (2023)
- [An Undetectable Watermark for Generative Image Models](https://mlsanity.com/paper/2410.07369/) (2024)
- [Video Seal: Open and Efficient Video Watermarking](https://mlsanity.com/paper/2412.09492/) (2024)
- [DVMark: A Deep Multiscale Framework for Video Watermarking](https://mlsanity.com/paper/2104.12734/) (2021)
- [Pseudorandom Error-Correcting Codes](https://mlsanity.com/paper/2402.09370/) (2024)
- [VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking](https://mlsanity.com/paper/2501.14195/) (2025)

## Built on by (most cited first, 18 of 18)

- [RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video](https://mlsanity.com/paper/2505.02064/) (2025)
- [AndroTMem: From Interaction Trajectories to Anchored Memory in Long-Horizon GUI Agents](https://mlsanity.com/paper/2603.18429/) (2026)
- [SHIFT: Stochastic Hidden-Trajectory Deflection for Removing Diffusion-based Watermark](https://mlsanity.com/paper/2603.29742/) (2026)
- [VideoGuard: Protecting Video Content from Unauthorized Editing](https://mlsanity.com/paper/2508.03480/) (2025)
- [SPDMark: Selective Parameter Displacement for Robust Video Watermarking](https://mlsanity.com/paper/2512.12090/) (2025)
- [SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion](https://mlsanity.com/paper/2603.02882/) (2026)
- [Towards Robust Content Watermarking Against Removal and Forgery Attacks](https://mlsanity.com/paper/2604.06662/) (2026)
- [Seeing vs. Believing: Evaluating the Language Bias of Open-Source MLLMs in Counter-Intuitive Scenes](https://mlsanity.com/paper/2601.07737/) (2026)
- [Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning](https://mlsanity.com/paper/2608.06938/) (2026)
- [MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation](https://mlsanity.com/paper/2608.10166/) (2026)
- [COVER: Codec-Robust Video Watermarking with Generative Video Priors](https://mlsanity.com/paper/2609.26236/) (2026)
- [mAVE: A Watermark for Joint Audio-Visual Generation Models](https://mlsanity.com/paper/2603.07090/) (2026)
- [Retrieval-Driven Training-Free AI-Generated Video Attribution](https://mlsanity.com/paper/2607.28955/) (2026)
- [Asymmetric Phase Coding Video Watermarking](https://mlsanity.com/paper/2608.29212/) (2026)
- [An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models](https://mlsanity.com/paper/2609.23586/) (2026)
- [Video Generation Models: A Survey of Post-Training and Alignment](https://mlsanity.com/paper/2610.00812/) (2026)
- [Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering](https://mlsanity.com/paper/2610.08137/) (2026)
- [Diffusion-Generated Image Watermarking: A Two-Axis Taxonomy and Three Protocol-Bounded Case Studies](https://mlsanity.com/paper/2610.09755/) (2026)

## Abstract (arXiv)

This work introduces VideoMark, a distortion-free robust watermarking framework for video diffusion models. As diffusion models excel in generating realistic videos, reliable content attribution is increasingly critical. However, existing video watermarking methods often introduce distortion by altering the initial distribution of diffusion variables and are vulnerable to temporal attacks, such as frame deletion, due to variable video lengths. VideoMark addresses these challenges by employing a pure pseudorandom initialization to embed watermarks, avoiding distortion while ensuring uniform noise distribution in the latent space to preserve generation quality. To enhance robustness, we adopt a frame-wise watermarking strategy with pseudorandom error correction (PRC) codes, using a fixed watermark sequence with randomly selected starting indices for each video. For watermark extraction, we propose a Temporal Matching Module (TMM) that leverages edit distance to align decoded messages with the original watermark sequence, ensuring resilience against temporal attacks. Experimental results show that VideoMark achieves higher decoding accuracy than existing methods while maintaining video quality comparable to watermark-free generation. The watermark remains imperceptible to attackers without the secret key, offering superior invisibility compared to other frameworks. VideoMark provides a practical, training-free solution for content attribution in diffusion-based video generation. Our code and data are available at hrefhttps://github.com/KYRIE-LI11/VideoMarkVideoMark.