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
The entire repo is organized as follows:
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.
The original model was trained with
we also test the repository with
Though small differences may be found, results are in general consistent.
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},
}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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