liuhao-97 / hinet

Official PyTorch implementation of "HiNet: Deep Image Hiding by Invertible Network" (ICCV 2021)

HiNet: Deep Image Hiding by Invertible Network

This repo is the official code for

• HiNet: Deep Image Hiding by Invertible Network.
  • Junpeng Jing, Xin Deng, Mai Xu, Jianyi Wang, Zhenyu Guan.

Published on ICCV 2021. By MC2 Lab @ Beihang University.

Dependencies and Installation

• Python 3 (Recommend to use Anaconda).
• PyTorch = 1.0.1 .
• See environment.yml for other dependencies.

Get Started

• Run python train.py for training.

• Run python test.py for testing.

• Set the model path (where the trained model saved) and the image path (where the image saved during testing) to your local path.

  line45: MODEL_PATH = ''

  line49: IMAGE_PATH = ''

Dataset

• In this paper, we use the commonly used dataset DIV2K, COCO, and ImageNet.

• For train or test on your own dataset, change the code in config.py:

  line30: TRAIN_PATH = ''

  line31: VAL_PATH = ''

Trained model

• Here we provide a trained model.

• Fill in the MODEL_PATH and the file name suffix before testing by the trained model.

• For example, if the model name is model.pt and its path is /home/usrname/Hinet/model/, set MODEL_PATH = '/home/usrname/Hinet/model/' and file name suffix = 'model.pt'.

Citation

If you find our paper or code useful for your research, please cite:

@InProceedings{Jing_2021_ICCV,
    author    = {Jing, Junpeng and Deng, Xin and Xu, Mai and Wang, Jianyi and Guan, Zhenyu},
    title     = {HiNet: Deep Image Hiding by Invertible Network},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4733-4742}
}