S^2F-Net:A Robust Spatial-Spectral Fusion Framework for Cross-Model AIGC Detection

TL;DR

S^2F-Net is a novel detection framework that leverages spectral discrepancies and frequency-domain artifacts to improve cross-model detection of AI-generated content, achieving high accuracy and strong generalization.

Contribution

The paper introduces S^2F-Net, a spectral-based detection framework with a learnable frequency attention module for robust cross-model AIGC detection.

Findings

Achieves 90.49% detection accuracy on AIGCDetectBenchmark

Significantly outperforms existing methods in cross-domain scenarios

Effectively exploits spectral discrepancies for generalization

Abstract

The rapid development of generative models has imposed an urgent demand for detection schemes with strong generalization capabilities. However, existing detection methods generally suffer from overfitting to specific source models, leading to significant performance degradation when confronted with unseen generative architectures. To address these challenges, this paper proposes a cross-model detection framework called S 2 F-Net, whose core lies in exploring and leveraging the inherent spectral discrepancies between real and synthetic textures. Considering that upsampling operations leave unique and distinguishable frequency fingerprints in both texture-poor and texture-rich regions, we focus our research on the detection of frequency-domain artifacts, aiming to fundamentally improve the generalization performance of the model. Specifically, we introduce a learnable frequency attention module that adaptively weights and enhances discriminative frequency bands by synergizing spatial texture analysis and spectral dependencies.On the AIGCDetectBenchmark, which includes 17 categories of generative models, S 2 F-Net achieves a detection accuracy of 90.49%, significantly outperforming various existing baseline methods in cross-domain detection scenarios.