Deepfake Geography: Detecting AI-Generated Satellite Images

TL;DR

This paper compares CNNs and Vision Transformers for detecting AI-generated satellite images, demonstrating ViTs' superior accuracy and robustness, and enhancing model interpretability to improve detection trustworthiness.

Contribution

It provides a comprehensive evaluation of ViTs versus CNNs for satellite deepfake detection, introducing interpretability methods specific to each architecture.

Findings

ViTs achieve 95.11% accuracy, outperforming CNNs at 87.02%.

ViTs are more robust in detecting synthetic imagery.

Interpretability methods reveal distinct detection behaviors.

Abstract

The rapid advancement of generative models such as StyleGAN2 and Stable Diffusion poses a growing threat to the authenticity of satellite imagery, which is increasingly vital for reliable analysis and decision-making across scientific and security domains. While deepfake detection has been extensively studied in facial contexts, satellite imagery presents distinct challenges, including terrain-level inconsistencies and structural artifacts. In this study, we conduct a comprehensive comparison between Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for detecting AI-generated satellite images. Using a curated dataset of over 130,000 labeled RGB images from the DM-AER and FSI datasets, we show that ViTs significantly outperform CNNs in both accuracy (95.11 percent vs. 87.02 percent) and overall robustness, owing to their ability to model long-range dependencies and global semantic structures. We further enhance model transparency using architecture-specific interpretability methods, including Grad-CAM for CNNs and Chefer's attention attribution for ViTs, revealing distinct detection behaviors and validating model trustworthiness. Our results highlight the ViT's superior performance in detecting structural inconsistencies and repetitive textural patterns characteristic of synthetic imagery. Future work will extend this research to multispectral and SAR modalities and integrate frequency-domain analysis to further strengthen detection capabilities and safeguard satellite imagery integrity in high-stakes applications.