DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection
Abstract
The deliberate manipulation of public opinion, especially through altered images, which are frequently disseminated through online social networks, poses a significant danger to society. To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset, comprising one million images from four major forgery categories: splicing, copy-move, enhancement and removal. This dataset enables an objective comparison of network architectures and can significantly reduce the time and effort of researchers preparing datasets.
Cite
@article{arxiv.2503.22417,
title = {DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection},
author = {David Fischinger and Martin Boyer},
journal= {arXiv preprint arXiv:2503.22417},
year = {2025}
}
Comments
Published at the 25th Irish Machine Vision and Image Processing Conference (IMVIP) --- Proceedings: https://iprcs.github.io/pdf/IMVIP2023_Proceeding.pdf --- Dataset download: https://zenodo.org/records/7326540/files/DF2023_train.zip https://zenodo.org/records/7326540/files/DF2023_val.zip Kaggle: https://www.kaggle.com/datasets/davidfischinger/df2023-digital-forensics-2023-dataset/data