# Video Seal: Open and Efficient Video Watermarking

arXiv: https://arxiv.org/abs/2412.09492 · HTML: https://mlsanity.com/paper/2412.09492/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 76 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 30 of 40)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Movie Gen: A Cast of Media Foundation Models](https://mlsanity.com/paper/2410.13720/) (2024)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [Geometric GAN](https://mlsanity.com/paper/1705.02894/) (2017)
- [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)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [Responsible Disclosure of Generative Models Using Scalable Fingerprinting](https://mlsanity.com/paper/2012.08726/) (2020)
- [RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification](https://mlsanity.com/paper/2404.14055/) (2024)
- [Distortion Agnostic Deep Watermarking](https://mlsanity.com/paper/2001.04580/) (2020)
- [Proactive Detection of Voice Cloning with Localized Watermarking](https://mlsanity.com/paper/2401.17264/) (2024)
- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2306.04744/) (2023)
- [Watermark Anything with Localized Messages](https://mlsanity.com/paper/2411.07231/) (2024)

## Built on by (most cited first, 30 of 48)

- [SynthID-Image: Image watermarking at internet scale](https://mlsanity.com/paper/2510.09263/) (2025)
- [VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models](https://mlsanity.com/paper/2504.16359/) (2025)
- [Watermarking Autoregressive Image Generation](https://mlsanity.com/paper/2506.16349/) (2025)
- [Pixel Seal: Adversarial-only training for invisible image and video watermarking](https://mlsanity.com/paper/2512.16874/) (2025)
- [Transferable Black-Box One-Shot Forging of Watermarks via Image Preference Models](https://mlsanity.com/paper/2510.20468/) (2025)
- [Watermarking Without Standards Is Not AI Governance](https://mlsanity.com/paper/2505.23814/) (2025)
- [Geometric Image Synchronization with Deep Watermarking](https://mlsanity.com/paper/2509.15208/) (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)
- [Guidance Watermarking for Diffusion Models](https://mlsanity.com/paper/2509.22126/) (2025)
- [LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization](https://mlsanity.com/paper/2604.23957/) (2026)
- [Content ARCs: Decentralized Content Rights in the Age of Generative AI](https://mlsanity.com/paper/2503.14519/) (2025)
- [Fast, Secure, and High-Capacity Image Watermarking with Autoencoded Text Vectors](https://mlsanity.com/paper/2510.00799/) (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)
- [Learning to Watermark in the Latent Space of Generative Models](https://mlsanity.com/paper/2601.16140/) (2026)
- [Towards Robust Content Watermarking Against Removal and Forgery Attacks](https://mlsanity.com/paper/2604.06662/) (2026)
- [X-SG2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks](https://mlsanity.com/paper/2502.10475/) (2025)
- [VIDSTAMP: A Temporally-Aware Watermark for Ownership and Integrity in Video Diffusion Models](https://mlsanity.com/paper/2505.01406/) (2025)
- [LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation](https://mlsanity.com/paper/2509.17773/) (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)
- [The Forensic Cost of Watermark Removal: From Dedicated Attacks to Image Editing](https://mlsanity.com/paper/2604.25491/) (2026)
- [Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles](https://mlsanity.com/paper/2605.06153/) (2026)
- [Compositional Adversarial Training for Robust Visual Watermarking](https://mlsanity.com/paper/2605.16720/) (2026)
- [Do Modern Post-Hoc Watermarking Methods Beat Broken-Arrows?](https://mlsanity.com/paper/2605.27135/) (2026)
- [Learning Probabilistic Embeddings for Unsupervised Action Segmentation](https://mlsanity.com/paper/2607.05263/) (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)
- [COVER: Codec-Robust Video Watermarking with Generative Video Priors](https://mlsanity.com/paper/2609.26236/) (2026)

## Abstract (arXiv)

The proliferation of AI-generated content and sophisticated video editing tools has made it both important and challenging to moderate digital platforms. Video watermarking addresses these challenges by embedding imperceptible signals into videos, allowing for identification. However, the rare open tools and methods often fall short on efficiency, robustness, and flexibility. To reduce these gaps, this paper introduces Video Seal, a comprehensive framework for neural video watermarking and a competitive open-sourced model. Our approach jointly trains an embedder and an extractor, while ensuring the watermark robustness by applying transformations in-between, e.g., video codecs. This training is multistage and includes image pre-training, hybrid post-training and extractor fine-tuning. We also introduce temporal watermark propagation, a technique to convert any image watermarking model to an efficient video watermarking model without the need to watermark every high-resolution frame. We present experimental results demonstrating the effectiveness of the approach in terms of speed, imperceptibility, and robustness. Video Seal achieves higher robustness compared to strong baselines especially under challenging distortions combining geometric transformations and video compression. Additionally, we provide new insights such as the impact of video compression during training, and how to compare methods operating on different payloads. Contributions in this work - including the codebase, models, and a public demo - are open-sourced under permissive licenses to foster further research and development in the field.