The fast evolution of generative models has heightened the demand for reliable detection of AI-generated images. To tackle this challenge, we introduce FUSE, a hybrid system that combines spectral features extracted through Fast Fourier Transform with semantic features obtained from the CLIP's Vision encoder. The features are fused into a joint representation and trained progressively in two stages. Evaluations on GenImage, WildFake, DiTFake, GPT-ImgEval and Chameleon datasets demonstrate strong generalization across multiple generators. Our FUSE (Stage 1) model demonstrates state-of-the-art results on the Chameleon benchmark. It also attains 91.36% mean accuracy on the GenImage dataset, 88.71% accuracy across all tested generators, and a mean Average Precision of 94.96%. Stage 2 training further improves performance for most generators. Unlike existing methods, which often perform poorly on high-fidelity images in Chameleon, our approach maintains robustness across diverse generators. These findings highlight the benefits of integrating spectral and semantic features for generalized detection of images generated by AI.
@article{arxiv.2512.21695,
title = {FUSE: Unifying Spectral and Semantic Cues for Robust AI-Generated Image Detection},
author = {Md. Zahid Hossain and Most. Sharmin Sultana Samu and Md. Kamrozzaman Bhuiyan and Farhad Uz Zaman and Md. Rakibul Islam},
journal= {arXiv preprint arXiv:2512.21695},
year = {2025}
}
Comments
accepted for publication in 2025 28th International Conference on Computer and Information Technology (ICCIT)