A Difference-in-Difference Approach to Detecting AI-Generated Images

Xinyi Qi, Kai Ye, Chengchun Shi, Ying Yang, Jin Zhu, Hongyi Zhou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 42964-42975

Diffusion models are able to produce AI-generated images that are almost indistinguishable from real ones, raising concerns about their potential misuse and posing 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. Code is available at https://github.com/Qixinyi1122-lucky/DID.