Abstract
Detecting forged remote sensing images is becoming increasingly critical, as such imagery plays a vital role in environmental monitoring, urban planning, and national security. While diffusion models have emerged as the dominant paradigm for image generation, their impact on remote sensing forgery detection remains underexplored. Existing benchmarks primarily target GAN-based forgeries or focus on natural images, limiting progress in this critical domain. To address this gap, we introduce RSFAKE-1M, a large-scale dataset of 500K forged and 500K real remote sensing images. The fake images are generated by ten diffusion models fine-tuned on remote sensing data, covering six generation conditions such as text prompts, structural guidance, and inpainting. This paper presents the construction of RSFAKE-1M along with a comprehensive experimental evaluation using both existing detectors and unified baselines. The results reveal that diffusion-based remote sensing forgeries remain challenging for current methods, and that models trained on RSFAKE-1M exhibit notably improved generalization and robustness. Our findings underscore the importance of RSFAKE-1M as a foundation for developing and evaluating next-generation forgery detection approaches in the remote sensing domain. The dataset and other supplementary materials are available at this https URL.
Abstract (translated)
检测伪造的遥感图像变得越来越重要，因为这类影像在环境监测、城市规划和国家安全中发挥着关键作用。虽然扩散模型已经成为图像生成的主要范式，但它们对遥感伪造检测的影响仍然未被充分探索。现有的基准测试主要针对基于GAN的伪造品或关注自然图像，这限制了这一关键领域的进展。为了解决这个缺口，我们引入了RSFAKE-1M，这是一个包含50万个伪造和50万个真实遥感图像的大规模数据集。这些假图像是通过十个在遥感数据上进行微调的扩散模型生成的，并涵盖了六种生成条件，如文本提示、结构指导和修补。本文介绍了RSFAKE-1M的数据构建方法及使用现有检测器与统一基线进行全面实验评估的结果。结果显示，基于扩散模型的遥感伪造品目前的方法仍然难以应对，而基于RSFAKE-1M训练的模型表现出显著改进的一般化能力和鲁棒性。我们的发现强调了RSFAKE-1M作为开发和评估下一代伪造检测方法在遥感领域中的重要基础的作用。该数据集和其他补充材料可以在 [此链接](https://example.com) 获取。
URL
https://arxiv.org/abs/2505.23283
