Diffusion models are able to produce AI-generated images that are almost indistinguishable from real ones. This raises concerns about their potential misuse and poses substantial challenges for detecting them. Many existing detectors rely on reconstruction error -- the difference between the input image and its reconstructed version -- as the basis for distinguishing real from fake images. However, these detectors become less effective as modern AI-generated images become increasingly similar to real ones. To address this challenge, we propose a novel difference-in-difference method. Instead of directly using the reconstruction error (a first-order difference), we compute the difference in reconstruction error -- a second-order difference -- for variance reduction and improving detection accuracy. Extensive experiments demonstrate that our method achieves strong generalization performance, enabling reliable detection of AI-generated images in the era of generative AI.
@article{arxiv.2602.23732,
title = {A Difference-in-Difference Approach to Detecting AI-Generated Images},
author = {Xinyi Qi and Kai Ye and Chengchun Shi and Ying Yang and Hongyi Zhou and Jin Zhu},
journal= {arXiv preprint arXiv:2602.23732},
year = {2026}
}