发表机构
Oak Ridge National Laboratory（橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对遥感领域缺乏合适卫星图像篡改定位基准数据集的问题，构建含60张图像的原型数据集，支持像素级定位等分析，以推动相关研究。
AI 中文摘要
鉴于生成式人工智能的进步，验证卫星图像的真实性已变得愈发关键。出于恶意目的生成的高度逼真的合成图像（深度伪造）可能会对遥感领域产生重大影响，该领域的数据是科学应用、规划、物流和监测的基本信息来源。遥感社区缺乏适合训练和评估检测及图像取证算法的高质量、细粒度篡改数据集。现有数据集存在不足，已有的数据集要么没有用于评估篡改定位的真值掩码，要么包含由GAN或扩散模型生成的完整图像，这不足以衡量定位性能。为解决这一缺口，我们描述了卫星图像篡改检测与定位的初步数据集构建过程及原型基准数据集。该数据集共包含60张图像，其中30张图像通过三种篡改类型（包括复制-粘贴拼接和扩散模型修复）进行了精心篡改，另有30张真实图像。每张图像都配有真值掩码和采集元数据，可实现像素级定位指标、图像元数据研究，以及篡改检测性能与图像采集参数之间关系的分析。我们描述了数据集构建过程并发布此初始版本，以支持图像取证和地理空间深度伪造检测领域的进一步研究。该原型数据集可在此处下载：https://URL。
英文摘要
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.
CommentsAccepted at IEEE IGARSS 2026
