DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection
Not on the map. Why?
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.
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
4 of 22 references.
References · 4
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2020Media Forensics and DeepFakes: an overviewcited by 209
- 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
- 2021Learning JPEG Compression Artifacts for Image Manipulation Detection and Localizationcited by 78
Built on by
Citing papers per year: 2025: 2.
2 papers.