DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

• URL: http://arxiv.org/abs/2503.22417v1
• Date: Fri, 28 Mar 2025 13:31:19 GMT
• Title: DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection
• Authors: David Fischinger, Martin Boyer,
• Abstract summary: The deliberate manipulation of public opinion, especially through altered images, poses a significant danger to society.<n>To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset.<n>This dataset enables an objective comparison of network architectures and can significantly reduce the time and effort of researchers preparing datasets.
• Score: 0.4143603294943439
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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