BusterNet_pytorch: Detecting Copy-Move Image Forgery with Source/Target Localization

Introduction

I reimplement a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet.

In this repository, we release many paper related things, including

• a pretrained BusterNet model (trained model at epoch 13)
• custom layers implemented in pytorch
• python demo notebook

Example

Dataset

USCISI-CMFD Dataset

Introduction

This copy-move forgery detection(CMFD) dataset relies on

• MIT SUN2012 Database
• MS COCO Dataset

More precisely, we synthesize a copy-move forgery sample using the following steps

1. select a sample in the two above dataset
2. select one of its object polygon
3. use both sample image and polgyon mask to synthesize a sample

More detailed description can be found in paper.

Folder Content

This USCISI-CMFD dataset folder contains the following things:

• api.py - USCISI-CMFD dataset API
• USCISI-CMFD Dataset - USCISI-CMFD LMDB dataset
  • Two versions are NOT included due to repo size limit. Please right click to download from the google drive.
    • USCISI-CMFD-Small - 100 samples, ~40MB
    • USCISI-CMFD-Full - 100K samples, ~100GB
  • After uncompressing the downloaded dataset, you should see the following files
    • data.mdb - sample LMDB data file
    • samples.keys - a file listing sample keys (each line is a key)
    • lock.mdb - sample LMDB locker file
• Demo.ipynb - a python notebook show the usage of API
• ReadMe.md - this file

NOTE due to the repository size limit, the full USCISI-CMFD dataset will be provided upon request.

Training

1. Download dataset to folder 'datasets' with link about. The ownership belong to yue_wu[at]isi.edu, therefor if you dont have accept permission. Please to contact him. (Optional) Download pretrained VGG16 at VGG16
2. Install independent package. pip install -r requirements.txt
3. Training: python train.py with custom argurments:

usage: Buster Net [-h] [-n NUM_WORKERS] [-b BATCH_SIZE] [--num_gpus NUM_GPUS]
                  [--freeze_layers [FREEZE_LAYERS [FREEZE_LAYERS ...]]]
                  [--lr LR] [--optim OPTIM] [--num_epochs NUM_EPOCHS]
                  [--val_interval VAL_INTERVAL]
                  [--save_interval SAVE_INTERVAL]
                  [--es_min_delta ES_MIN_DELTA] [--es_patience ES_PATIENCE]
                  [--lmdb_dir LMDB_DIR] [--log_path LOG_PATH]
                  [-w LOAD_WEIGHTS] [--saved_path SAVED_PATH]

4. Try predict in demo.ipynb

Citation

If you use the provided code or data in any publication, please kindly cite the following paper.

@inproceedings{wu2018eccv,
  title={BusterNet: Detecting Image Copy-Move Forgery With Source/Target Localization},
  author={Wu, Yue, and AbdAlmageed, Wael and Natarajan, Prem},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2018},
  organization={Springer},
}

Contact

• Name: Nguyen Thanh Dat
• Email: ntdat017[at]gmail.com

License

The Software is made available for academic or non-commercial purposes only. The license is for a copy of the program for an unlimited term. Individuals requesting a license for commercial use must pay for a commercial license.

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  University of Southern California 
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For commercial license pricing and annual commercial update and support pricing, please contact:

  Rakesh Pandit USC Stevens Institute for Innovation 
  University of Southern California 
  1150 S. Olive Street, Suite 2300
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  Tel: +1 213-821-3552
  Fax: +1 213-821-5001 
  Email: [email protected] and ccto: [email protected]