FUSE: Unifying Spectral and Semantic Cues for Robust AI-Generated Image Detection

TL;DR

FUSE is a hybrid detection system that combines spectral Fourier features with semantic features from CLIP, achieving state-of-the-art results in identifying AI-generated images across diverse datasets and generators.

Contribution

The paper introduces FUSE, a novel hybrid approach that fuses spectral and semantic features for robust AI-generated image detection, outperforming existing methods.

Findings

State-of-the-art results on Chameleon benchmark

91.36% accuracy on GenImage dataset

Robust detection across multiple generators

Abstract

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.