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

Introduction

We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet. Unlike previous efforts, BusterNet is a pure, end-to-end trainable, deep neural network solution. It features a two-branch architecture followed by a fusion module. The two branches localize potential manipulation regions via visual artifacts and copy-move regions via visual similarities, respectively. To the best of our knowledge, this is the first CMFD algorithm with discernibility to localize source/target regions.

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

a pretrained BusterNet model
custom layers implemented in keras-tensorflow
CASIA-CMFD, CoMoFoD-CMFD, and USCISI-CMFD dataset
python notebook to reproduce paper results

Repo Organization

The entire repo is organized as follows:

Data - host all datasets
- *CASIA-CMFD
- *CoMoFoD-CMFD
- *USCISI-CMFD
Model - host all model files
ReadMe.md - this file

Due to the size limit, we can't host all dataset in repo. For those large ones, we host them externally. *indicated dataset requires to be downloaded seperately. Please refer to the document of each dataset for more detailed downloading instructions.

Python/Keras/Tensorflow

The original model was trained with

keras.version = 2.0.7
tensorflow.version = 1.1.0

we also test the repository with

keras.version = 2.2.2
tensorflow.version = 1.8.0

Though small differences may be found, results are in general consistent.

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: Yue Wu
Email: yue_wu[at]isi.edu

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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