Dual Frequency Branch Framework With Reconstructed Sliding Windows Attention for AI-Generated Image Detection

Summary

The authors present a detector for AI-generated images that combines a sliding-window attention mechanism, which models relationships between neighboring elements within local regions, with a dual frequency branch that uses four DWT subbands and the phase part of FFT. Evaluated on datasets covering images from 65 generative models, including GAN and diffusion-based ones, the method improves detection accuracy by 2.13% over state-of-the-art approaches.