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

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.

🔧 Dependencies and Installation

• Python 3.6
• PyTorch >= 1.4.0
• numpy
• skimage
• cv2

⏬ Download Models

The pre-trained models are available at:

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

🤗 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 [email protected].

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}
}