遥感领域缺精细的篡改定位数据集，这个原型集合了拼接和扩散修复两类篡改，还带掩码和元数据，做取证研究可以直接拿来测。
该论文提出一个用于卫星图像篡改检测与定位的原型基准数据集，包含60张图像，其中30张经复制粘贴拼接或扩散模型修复等方式篡改，另30张为真实图像。每张图像都配有像素级真值掩码和采集元数据，支持定位性能评估。数据集已发布在HuggingFace平台，可免费下载。
Towards a satellite image manipulation and deepfake localization benchmark dataset
Verifying the authenticity of satellite imagery has become increasingly critical given advances in generative artificial intelligence. Highly realistic synthetic imagery produced for malicious purposes (deepfakes) can have major consequences in the remote sensing domain, where this data is a fundamental source of information for science applications, planning, logistics, and monitoring. The remote sensing community lacks high-quality, fine-grained manipulation datasets suitable for training and evaluating detection and image forensics algorithms. Existing datasets are lacking and those that do exist either provide no ground truth masks for evaluating manipulation localization, or consist of entire images generated by GANs or diffusion models, which are inadequate for measuring localization performance. To address this gap, we describe a preliminary dataset construction process and prototype benchmark dataset for satellite image manipulation detection and localization. The dataset contains 60 images total, with 30 images carefully manipulated using three manipulation types including copy-paste splicing and diffusion model inpainting, and 30 authentic images. Each image is accompanied by a ground-truth mask and acquisition metadata, enabling both pixel-level localization metrics, image metadata studies, and analyses of how manipulation detection performance relates to image collection parameters. We describe the dataset construction process and present this initial release to support further research in image forensics and geospatial deepfake detection. The prototype dataset can be downloaded at https://huggingface.co/datasets/geodf/fmow-fake-small.