Code for "A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection" The paper can be found on arxiv.

Download the model weights in ./model/E2E folder from here

To run the code on given test dataset, type:

python test.py -m=FUSION

This will run the fusion of the three models proposed in the paper (E2E-FUSION) on images in ./test/ directory.
For running on a single modality, change 'mode' parameter: 'RGB'-'N'-'RGN'

To run the code on your own dataset, type:

python test.py --te=PATH_TO_YOUR_DATASET

Where PATH_TO_YOUR_DATASET is organized with two subfolder: '0' contains pristine images and '1' contains forged images.
Modify the test.py file according to your needs.

For any problem or comment, do not hesitate to contact me.

@misc{marra2019e2e,
    title={A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection},
    author={Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, Giovanni Poggi},
    year={2019},
    eprint={1909.06751},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Copyright (c) 2019 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').

All rights reserved.

This software should be used, reproduced and modified only for informational and nonprofit purposes.

By downloading and/or using any of these files, you implicitly agree to all the terms of the license, as specified in the document LICENSE.txt (included in this package)