VideoSeal: Open and Efficient Video Watermarking

I. Zeki Yalniz, Pierre Fernandez, Hady Elsahar, Alexandre Mourachko

Abstract:

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