# VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking

arXiv: https://arxiv.org/abs/2501.14195 · HTML: https://mlsanity.com/paper/2501.14195/

## Key facts

- Cited by 34 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 33 of its 41 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)
- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [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)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation](https://mlsanity.com/paper/2304.09790/) (2023)
- [Learning Joint Spatial-Temporal Transformations for Video Inpainting](https://mlsanity.com/paper/2007.10247/) (2020)
- [Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models](https://mlsanity.com/paper/2405.04233/) (2024)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models](https://mlsanity.com/paper/2404.04956/) (2024)
- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [An Undetectable Watermark for Generative Image Models](https://mlsanity.com/paper/2410.07369/) (2024)
- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [DVMark: A Deep Multiscale Framework for Video Watermarking](https://mlsanity.com/paper/2104.12734/) (2021)

## Built on by (most cited first, 30 of 34)

- [VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models](https://mlsanity.com/paper/2504.16359/) (2025)
- [Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking](https://mlsanity.com/paper/2505.12667/) (2025)
- [Mask Image Watermarking](https://mlsanity.com/paper/2504.12739/) (2025)
- [VideoMarkBench: Benchmarking Robustness of Video Watermarking](https://mlsanity.com/paper/2505.21620/) (2025)
- [Video Signature: Implicit Watermarking for Video Diffusion Models](https://mlsanity.com/paper/2506.00652/) (2025)
- [MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models](https://mlsanity.com/paper/2509.10569/) (2025)
- [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)
- [RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection](https://mlsanity.com/paper/2512.10248/) (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)
- [Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis](https://mlsanity.com/paper/2506.23263/) (2025)
- [VIDSTAMP: A Temporally-Aware Watermark for Ownership and Integrity in Video Diffusion Models](https://mlsanity.com/paper/2505.01406/) (2025)
- [MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection](https://mlsanity.com/paper/2505.24267/) (2025)
- [Unifying Watermarking via Dimension-Aware Mapping](https://mlsanity.com/paper/2602.03373/) (2026)
- [SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models](https://mlsanity.com/paper/2603.00194/) (2026)
- [SWIFT: Sliding Window Reconstruction for Few-Shot Training-Free Generated Video Attribution](https://mlsanity.com/paper/2603.08536/) (2026)
- [Rethinking Forgery Attacks on Semantic Watermarks in Black-Box Settings: A Geometric Distortion Perspective](https://mlsanity.com/paper/2606.29807/) (2026)
- [FARI: Robust One-Step Inversion for Watermarking in Diffusion Models](https://mlsanity.com/paper/2607.26723/) (2026)
- [MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation](https://mlsanity.com/paper/2608.10166/) (2026)
- [Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination](https://mlsanity.com/paper/2608.14391/) (2026)
- [WaTeRFlow: Watermark Temporal Robustness via Flow Consistency](https://mlsanity.com/paper/2512.19048/) (2025)
- [Beyond Detection: Multi-Scale Hidden-Code for Natural Image Deepfake Recovery and Factual Retrieval](https://mlsanity.com/paper/2602.22759/) (2026)
- [mAVE: A Watermark for Joint Audio-Visual Generation Models](https://mlsanity.com/paper/2603.07090/) (2026)
- [Moir'e Video Authentication: A Physical Signature Against AI Video Generation](https://mlsanity.com/paper/2604.01654/) (2026)
- [From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI](https://mlsanity.com/paper/2605.16471/) (2026)
- [FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMs](https://mlsanity.com/paper/2606.22875/) (2026)
- [Retrieval-Driven Training-Free AI-Generated Video Attribution](https://mlsanity.com/paper/2607.28955/) (2026)
- [IP Protection in the Era of Visual Generative AI: A Survey](https://mlsanity.com/paper/2608.14730/) (2026)

## Abstract (arXiv)

Artificial Intelligence Generated Content (AIGC) has advanced significantly, particularly with the development of video generation models such as text-to-video (T2V) models and image-to-video (I2V) models. However, like other AIGC types, video generation requires robust content control. A common approach is to embed watermarks, but most research has focused on images, with limited attention given to videos. Traditional methods, which embed watermarks frame-by-frame in a post-processing manner, often degrade video quality. In this paper, we propose VideoShield, a novel watermarking framework specifically designed for popular diffusion-based video generation models. Unlike post-processing methods, VideoShield embeds watermarks directly during video generation, eliminating the need for additional training. To ensure video integrity, we introduce a tamper localization feature that can detect changes both temporally (across frames) and spatially (within individual frames). Our method maps watermark bits to template bits, which are then used to generate watermarked noise during the denoising process. Using DDIM Inversion, we can reverse the video to its original watermarked noise, enabling straightforward watermark extraction. Additionally, template bits allow precise detection for potential temporal and spatial modification. Extensive experiments across various video models (both T2V and I2V models) demonstrate that our method effectively extracts watermarks and detects tamper without compromising video quality. Furthermore, we show that this approach is applicable to image generation models, enabling tamper detection in generated images as well. Codes and models are available at https://github.com/hurunyi/VideoShield.