# DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

arXiv: https://arxiv.org/abs/2503.22417 · HTML: https://mlsanity.com/paper/2503.22417/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)

## Built on by (most cited first, 2 of 2)

- [Can Multi-modal (reasoning) LLMs detect document manipulation?](https://mlsanity.com/paper/2508.11021/) (2025)
- [VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics](https://mlsanity.com/paper/2512.15512/) (2025)

## Abstract (arXiv)

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.