MBRS

MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression

Zhaoyang Jia, Han Fang, Weiming Zhang (from University of Science and Technology of China)

This is the source code of paper MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression, which is received by ACM MM' 21 (oral). Please contact me in issue page or email jzy_ustc@mail.ustc.edu.cn if you find bugs. Thanks!

Requirements

We used these packages/versions in the development of this project.

Pytorch 1.5.0
torchvision 0.3.0a0+ec20315
kornia 0.3.0
numpy 1.16.4
Pillow 6.0.0
scipy 1.3.0

Dataset prepare

Please download ImageNet or COCO datasets, and push them into datasets folder like this :

├── datasets
│   ├── train
│   │   ├── xxx.jpg
│   │   ├── ...
│   ├── test
│   │   ├── xxx.jpg
│   │   ├── ...
│   ├── validation
│   │   ├── xxx.jpg
│   │   ├── ...
├── ...
├── results

For more details about the used datasets, please read the original paper.

Pretrained Models

Please download pretrained models in Google Drive and put the in path results/xxx/models/. (xxx is the name of the project, e.g. MBRS_256_m256)

Train

Change the settings in json file train_settings.json, then run :

python train.py

The logging file and results will be saved at results/xxx/

Test

Change the settings in json file test_settings.json, then run :

python test.py

The logging file and results will be saved at results/xxx/

Citation

Please cite our paper if you find this repo useful!

@inproceedings{jia2021mbrs,
  title={MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression},
  author={Zhaoyang  Jia, Han Fang and Weiming Zhang},
  booktitle={arXiv:2108.08211},
  year={2021}
}

Contact: jzy_ustc@mail.ustc.edu.cn

coriverchen/MBRS