A lightweight multi-scale fourier enhancement network for image forgery detection

The proliferation of highly realistic forged images, driven by advances in generative models such as GANs and diffusion models, poses serious challenges to information security and digital forensics. While existing detection methods leveraging spatial-domain features have shown progress, they often struggle to generalize to ever-evolving forgery techniques–particularly AI-generated content. To address this limitation, we propose a novel Multi-scale Fourier Enhancement Network that integrates frequency-domain analysis with multi-scale feature learning. Our approach incorporates a lightweight Fourier Enhancement Module into a ConvNeXt backbone, enabling the capture of frequency anomalies introduced by forgery operations. Combined with a multi-scale fusion mechanism and a hybrid loss function, the model achieves robust feature learning from local textures to global semantics. Extensive experiments on multiple benchmarks demonstrate that our method achieves superior accuracy and robustness while maintaining computational efficiency, and shows promising cross-generator generalization performance on unseen generative sources.