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Large-capacity and Flexible Video Steganography via Invertible Neural Network (CVPR 2023)
Chong Mou, Youmin Xu, Jiechong Song, Chen Zhao, Bernard Ghanem, Jian Zhang
Official implementation of Large-capacity and Flexible Video Steganography via Invertible Neural Network.
Introduction
<p align="center"> <img src="assets/overview.PNG"> </p> <!-- <div align="center"> -->Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these weaknesses, we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN) in this paper. For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN). Our method can hide/recover 7 secret videos in/from 1 cover video with promising performance. For flexibility, we propose a key-controllable scheme, enabling different receivers to recover particular secret videos from the same cover video through specific keys. Moreover, we further improve the flexibility by proposing a scalable strategy in multiple videos hiding, which can hide variable numbers of secret videos in a cover video with a single model and a single training session. Extensive experiments demonstrate that with the significant improvement of the video steganography performance, our proposed LF-VSN has high security, large hiding capacity, and flexibility.
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🔧 Dependencies and Installation
- Python 3.6
- PyTorch >= 1.4.0
- numpy
- skimage
- cv2
⏬ Download Models
The pre-trained models are available at:
<!-- <div align="center"> -->Mode | Download link |
---|---|
One video hiding | Google Drive |
Two video hiding | Google Drive |
Three video hiding | Google Drive |
Four video hiding | Google Drive |
Five video hiding | Google Drive |
Six video hiding | Google Drive |
Seven video hiding | Google Drive |
Data Preparing
Please download the training and evaluation dataset from Vimeo-90K.
Train
Training the desired model by changing the config file.
python train.py -opt options/train/train_LF-VSN_1video.yml
Test
Testing the desired model by changing the config file.
python test.py -opt options/train/train_LF-VSN_1video.yml
Qualitative Results
<p align="center"> <img src="assets/performance.PNG"> </p>🤗 Acknowledgements
This code is built on MIM-VRN (PyTorch). We thank the authors for sharing their codes of MIMO-VRN.
:e-mail: Contact
If you have any question, please email eechongm@gmail.com
.
Citation
If you find our work helpful in your resarch or work, please cite the following paper.
@inproceedings{mou2023lfvsn,
title={Large-capacity and Flexible Video Steganography via Invertible Neural Network},
author={Chong Mou, Youmin Xu, Jiechong Song, Chen Zhao, Bernard Ghanem, Jian Zhang},
booktitle={CVPR},
year={2023}
}