Official repository of "DeepMIH: Deep Invertible Network for Multiple Image Hiding", TPAMI 2022.

Junpeng Jing

Last update: Nov 22, 2022

Overview

DeepMIH: Deep Invertible Network for Multiple Image Hiding (TPAMI 2022)

This repo is the official code for

DeepMIH: Deep Invertible Network for Multiple Image Hiding [论文速览]
- Zhenyu Guan¹, Junpeng Jing¹(co-first), Xin Deng, Mai Xu, Lai Jiang, Zhou Zhang, Yipeng Li.

Published on IEEE Transactions of Pattern Analysis and Machine Intelligence (TPAMI 2022). @ Beihang University.

1. Pre-request

1.1 Dependencies and Installation

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

1.2 Dataset

In this paper, we use the commonly used dataset DIV2K, COCO, and ImageNet.
For train or test on your own path, change the code in config.py:
line50: TRAIN_PATH_DIV2K = ''
line51: VAL_PATH_DIV2K = ''
line54: VAL_PATH_COCO = ''
line55: TEST_PATH_COCO = ''
line57: VAL_PATH_IMAGENET = ''

2. Test

Here we provide a trained model.
Download and update the MODEL_PATH and the file name suffix before testing by the trained model.
For example, if the model name is model_checkpoint_03000_1.pt, model_checkpoint_03000_2.pt, model_checkpoint_03000_3.pt,
and its path is /home/usrname/DeepMIH/model/,
set:
PRETRAIN_PATH = '/home/usrname/DeepMIH/model/',
PRETRAIN_PATH_3 = '/home/usrname/DeepMIH/model/',
file name suffix = 'model_checkpoint_03000'.
Check the dataset path is correct.
Create an image path to save the generated images. Update TEST_PATH.
Run test_oldversion.py.

3. Train

Create a path to save the trained models and update MODEL_PATH.
Check the optim parameters in config.py is correct. Make sure the sub-model(net1, net2, net3...) you want to train is correct.
Run train_old_version.py. Following the Algorithm 1 to train the model.
Note: DeepMIH may be hard to train. The model may suffer from explosion. Our solution is to stop the training process at a normal node and abate the learning rate. Then, continue to train the model.

4. Further explanation

In the train_old_version.py at line 223:
rev_secret_dwt_2 = rev_dwt_2.narrow(1, 4 * c.channels_in, 4 * c.channels_in) # channels = 12,
the recovered secret image_2 is obtained by spliting the middle 12 channels of the varible rev_dwt_2. However, in the forward process_2, the input is obtained by concatenating (stego, imp, secret_2) together. This means that the original code train_old_version.py has a bug on recovery process (the last 12 channels of the varible rev_dwt_2 should be splited to be the recovered secret image_2, instead of the middle 12 one). We found that in this way the network is still able to converge, thus we keep this setting in the test process.
We also offer a corrected version train.py (see line 225) and test.py. You can also train your own model in this way.

Feel free to contact: [email protected].

Citation

If you find this repository helpful, you may cite:

@ARTICLE{9676416,
  author={Guan, Zhenyu and Jing, Junpeng and Deng, Xin and Xu, Mai and Jiang, Lai and Zhang, Zhou and Li, Yipeng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={DeepMIH: Deep Invertible Network for Multiple Image Hiding}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2022.3141725}}

Comments

Are the results in HiNet and DeepMIH tested on RGB or Y,U,V?

Hi! Thank you for sharing your code! Are the results in HiNet and DeepMIH tested on RGB or Y,U,V? When we reproduced HiNet, we found that the results in the paper could not be reproduced, and the PSNR in the log file given by HiNet's official code did not reach that in the paper. When we use the provided DeepMIH model to test, PSNR also does not achieve the results in the paper. Since we were all testing in RGB, and the code included testing on Y, this was questionable.

opened by bumandpunk 2
net3 problem

Hi! Thanks for the code and I have a problem. When the number of pictures increases to 3 or 4, do the parameters of net3 share in the following training process? Or we need another network? Thanks.

opened by DrinkWaterOrange 0
call for codes

Hello, I am very interested in your work. I noticed that your work is compared with the previous related work, but the ISN code is not open source. Do you reproduce it yourself or find the original author. If you reproduce it yourself, can you tell me what the reference code is? If the original author sent the code to you, can you share it?

opened by crisjade 0
Hi, thanks for your interest. We only polish and offer the code and the model for DeepMIH with 2 images hidden, which contains the main experiments in the paper. For DeepMIH-P, it was added in the Response process, and we only added a commonly used vgg_loss, which can be found in lots of low-level works. Specifically, we mainly referred to [HiDDeN](https://github.com/ando-khachatryan/HiDDeN).

Hi, thanks for your interest. We only polish and offer the code and the model for DeepMIH with 2 images hidden, which contains the main experiments in the paper. For DeepMIH-P, it was added in the Response process, and we only added a commonly used vgg_loss, which can be found in lots of low-level works. Specifically, we mainly referred to HiDDeN.

Originally posted by @TomTomTommi in https://github.com/TomTomTommi/DeepMIH/issues/1#issuecomment-1050446436

opened by excellent666 0
your training set

Hi! Thank you for sharing your code! I found that the training set of your model in your paper uses the DIV2k dataset, but the training set of this dataset has only 800 images. I want to determine whether the pre-trained model you provided only uses these 800 images for training?

opened by kerryhhh 1

Owner

Junpeng Jing

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Official repository of "DeepMIH: Deep Invertible Network for Multiple Image Hiding", TPAMI 2022.

Related tags

Overview

DeepMIH: Deep Invertible Network for Multiple Image Hiding (TPAMI 2022)

1. Pre-request

1.1 Dependencies and Installation

1.2 Dataset

2. Test

3. Train

4. Further explanation

Citation

Comments

Are the results in HiNet and DeepMIH tested on RGB or Y,U,V?

net3 problem

call for codes

your training set

Owner

Junpeng Jing

Imposter-detector-2022 - HackED 2022 Team 3IQ - 2022 Imposter Detector

DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021

The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)

PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).

[TPAMI 2021] iOD: Incremental Object Detection via Meta-Learning

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The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

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