A Difference-in-Difference Approach to Detecting AI-Generated Images
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912930072625152 |
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| author | Qi, Xinyi Ye, Kai Shi, Chengchun Yang, Ying Zhou, Hongyi Zhu, Jin |
| author_facet | Qi, Xinyi Ye, Kai Shi, Chengchun Yang, Ying Zhou, Hongyi Zhu, Jin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23732 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Difference-in-Difference Approach to Detecting AI-Generated Images Qi, Xinyi Ye, Kai Shi, Chengchun Yang, Ying Zhou, Hongyi Zhu, Jin Computer Vision and Pattern Recognition 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. |
| title | A Difference-in-Difference Approach to Detecting AI-Generated Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.23732 |