Yulin Zhang, Jiangqun Ni, Wenkang Su, and Xin Liao
This repository is a code release for the paper found here. The paper focus on deep video watermarking with temporal robustness and invisibility. The main contributions are the proposed temporal-associated feature extraction block (TAsBlock), differentiable video compression simulator(DiffH264), and spatial/temporal mask loss.
If you find our work useful, please consider citing:
@inproceedings{zhang2023novel,
title={A Novel Deep Video Watermarking Framework with Enhanced Robustness to H. 264/AVC Compression},
author={Zhang, Yulin and Ni, Jiangqun and Su, Wenkang and Liao, Xin},
booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
pages={8095--8104},
year={2023}
}
The models are free for non-commercial and scientific research purpose. Please mail us for further licensing terms.
- The optic flow estimation code is based on sniklaus/pytorch-spynet. The original paper is
@inproceedings{Ranjan_CVPR_2017,
author = {Ranjan, Anurag and Black, Michael J.},
title = {Optical Flow Estimation Using a Spatial Pyramid Network},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2017}
}
- The code for intra compression and residual compression is based on mlomnitz/DiffJPEG.