衛星画像の改ざんとディープフェイク位置特定のためのベンチマークデータセット構築に向けて
Towards a satellite image manipulation and deepfake localization benchmark dataset
衛星画像の改ざん検出と位置特定のための、高品質なデータセットが不足している問題に対処するため、コピーペーストや拡散モデルによるインペインティングを含む改ざん画像と本物画像からなるプロトタイプデータセットを構築した。
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著者: Jacob Arndt, Debvrat Varshney, Philipe Dias, Nivedita Nukavarapu
分類: cs.CV, cs.AI
原文アブストラクト
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.