# DVMark: A Deep Multiscale Framework for Video Watermarking

arXiv: https://arxiv.org/abs/2104.12734 · HTML: https://mlsanity.com/paper/2104.12734/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 30 of 46)

- [RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification](https://mlsanity.com/paper/2404.14055/) (2024)
- [SoK: Watermarking for AI-Generated Content](https://mlsanity.com/paper/2411.18479/) (2024)
- [Video Seal: Open and Efficient Video Watermarking](https://mlsanity.com/paper/2412.09492/) (2024)
- [VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking](https://mlsanity.com/paper/2501.14195/) (2025)
- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [V2A-Mark: Versatile Deep Visual-Audio Watermarking for Manipulation Localization and Copyright Protection](https://mlsanity.com/paper/2404.16824/) (2024)
- [WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights](https://mlsanity.com/paper/2405.02066/) (2024)
- [GS-Hider: Hiding Messages into 3D Gaussian Splatting](https://mlsanity.com/paper/2405.15118/) (2024)
- [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://mlsanity.com/paper/2107.09287/) (2021)
- [GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19895/) (2024)
- [VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models](https://mlsanity.com/paper/2504.16359/) (2025)
- [Pixel Seal: Adversarial-only training for invisible image and video watermarking](https://mlsanity.com/paper/2512.16874/) (2025)
- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (2024)
- [SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography](https://mlsanity.com/paper/2503.06118/) (2025)
- [Yuan: Yielding Unblemished Aesthetics Through A Unified Network for Visual Imperfections Removal in Generated Images](https://mlsanity.com/paper/2501.08505/) (2025)
- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking](https://mlsanity.com/paper/2505.12667/) (2025)
- [Steganalysis on Digital Watermarking: Is Your Defense Truly Impervious?](https://mlsanity.com/paper/2406.09026/) (2024)
- [LVMark: Robust Watermark for Latent Video Diffusion Models](https://mlsanity.com/paper/2412.09122/) (2024)
- [GS-Marker: Generalizable and Robust Watermarking for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.18718/) (2025)
- [A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion](https://mlsanity.com/paper/2206.01094/) (2022)
- [On the Coexistence and Ensembling of Watermarks](https://mlsanity.com/paper/2501.17356/) (2025)
- [Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities](https://mlsanity.com/paper/2504.03765/) (2025)
- [RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection](https://mlsanity.com/paper/2512.10248/) (2025)
- [SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion](https://mlsanity.com/paper/2603.02882/) (2026)
- [Splats in Splats: Robust and Effective 3D Steganography towards Gaussian Splatting](https://mlsanity.com/paper/2412.03121/) (2024)
- [Building an Invisible Shield for Your Portrait against Deepfakes](https://mlsanity.com/paper/2305.12881/) (2023)
- [SLIC: Secure Learned Image Codec through Compressed Domain Watermarking to Defend Image Manipulation](https://mlsanity.com/paper/2410.15075/) (2024)
- [VIDSTAMP: A Temporally-Aware Watermark for Ownership and Integrity in Video Diffusion Models](https://mlsanity.com/paper/2505.01406/) (2025)
- [Unifying Watermarking via Dimension-Aware Mapping](https://mlsanity.com/paper/2602.03373/) (2026)

## Abstract (arXiv)

Video watermarking embeds a message into a cover video in an imperceptible manner, which can be retrieved even if the video undergoes certain modifications or distortions. Traditional watermarking methods are often manually designed for particular types of distortions and thus cannot simultaneously handle a broad spectrum of distortions. To this end, we propose a robust deep learning-based solution for video watermarking that is end-to-end trainable. Our model consists of a novel multiscale design where the watermarks are distributed across multiple spatial-temporal scales. It gains robustness against various distortions through a differentiable distortion layer, whereas non-differentiable distortions, such as popular video compression standards, are modeled by a differentiable proxy. Extensive evaluations on a wide variety of distortions show that our method outperforms traditional video watermarking methods as well as deep image watermarking models by a large margin. We further demonstrate the practicality of our method on a realistic video-editing application.