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

• URL: http://arxiv.org/abs/2602.23732v1
• Date: Fri, 27 Feb 2026 06:57:39 GMT
• Title: A Difference-in-Difference Approach to Detecting AI-Generated Images
• Authors: Xinyi Qi, Kai Ye, Chengchun Shi, Ying Yang, Hongyi Zhou, Jin Zhu,
• Abstract summary: Diffusion models are able to produce AI-generated images that are almost indistinguishable from real ones.<n>This raises concerns about their potential misuse and poses substantial challenges for detecting them.<n>We propose a novel difference-in-difference method for distinguishing real from fake images.
• Score: 12.73070476746517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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