发布百万级图像伪造检测数据集，助力打击网络虚假信息。
DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection
- 构建包含百万张图像的伪造数据集，覆盖四类主流篡改手法。
- 支持模型架构客观对比，大幅降低研究者数据准备成本。
- 适合从事图像取证、AI安全与内容可信度研究的学者使用。
通过伪造图像在社交媒体中传播以操纵公众舆论，对社会构成严重威胁。为从技术层面应对该问题，我们发布数字取证2023（DF2023）训练与验证数据集，包含一百万张来自四种主要伪造类别（拼接、复制-移动、增强、删除）的图像。该数据集可实现网络架构的客观比较，显著减少研究人员准备数据所需的时间与精力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。