AI-Generated Disaster Image Detection: A Dataset and an Exploration of Key Challenges

Abstract

The rapid advancement of generative artificial intelligence (AI) has enabled the creation of highly realistic disaster imagery, posing significant threats to information authenticity during crisis events. To address this emerging challenge, we present AIG-DI, the first dataset specifically designed for detecting AI-generated disaster images. The dataset is systematically constructed across multiple disaster types, image content categories, and generative models, enabling comprehensive evaluation under diverse conditions. We conduct an in-depth empirical study using two representative detectors—NPR (a spatial-domain detector) and FreDect (a frequency-domain detector)—to investigate their detection performance, generalization ability, and robustness under realistic perturbations. Experimental results reveal three key findings: (1) detectors trained on generic datasets struggle to detect AI-generated disaster imagery due to domain-specific texture and semantic shifts; (2) incorporating even a small amount of disaster-specific synthetic images during training significantly boosts accuracy and generalization, highlighting the value of domain-specific data for rapid adaptation; and (3) image perturbations remain a critical vulnerability, even with perturbation-aware training. This work not only provides the first benchmark for AI-generated disaster image detection but also uncovers fundamental challenges in ensuring visual content authenticity for disaster response. The code and data will be made publicly available upon acceptance of the paper.