RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries

TL;DR

This paper introduces RSFAKE-1M, a large-scale dataset of remote sensing images with forged and real examples generated by diffusion models, to advance detection methods in this critical domain.

Contribution

The creation of RSFAKE-1M, the first large-scale dataset for diffusion-generated remote sensing forgery detection, and comprehensive evaluation of detection methods using this dataset.

Findings

Diffusion-based forgeries are challenging for current detectors.

Models trained on RSFAKE-1M show improved generalization and robustness.

The dataset enables development of next-generation forgery detection approaches.

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 https://huggingface.co/datasets/TZHSW/RSFAKE/.