R2BD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images

Summary

R$^{2}$2BD is a fake image detection framework that uses a unified reconstruction model, G-LDM, which simulates the generation behaviors of VAEs, GANs and diffusion models. Its residual bias calculation module distinguishes real from fake images in a single inference step, where existing reconstruction-based methods typically require 20$+$+ steps. Across 10 public datasets it is reported to be over 22× faster while achieving superior accuracy, and in cross-dataset evaluations it outperforms state-of-the-art methods by an average of 13.87%.