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This limitation stems from the distinctive forensic characteristics of medical images, a result of their imaging process.<\/jats:p>\n          <jats:p>In this work, we propose a novel anomaly detector for medical imagery based on diffusion models. Normally, diffusion models are used to generate images. However, we show how a similar process can be used to detect synthetic content by making a model reverse the diffusion on a suspected image. We evaluate our method on the task of detecting fake tumors injected and removed from CT and MRI scans. Our method significantly outperforms other state-of-the-art unsupervised detectors with an increased AUC of 0.9 from 0.79 for injection and of 0.96 from 0.91 for removal on average. We also explore our hypothesis using AI explainability tools and publish both our code and new medical deepfake datasets to encourage further research into this domain.<\/jats:p>","DOI":"10.1145\/3744656","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T12:04:52Z","timestamp":1749816292000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1652-7614","authenticated-orcid":false,"given":"Fred M.","family":"Grabovski","sequence":"first","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3075-8176","authenticated-orcid":false,"given":"Lior","family":"Yasur","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5987-4402","authenticated-orcid":false,"given":"Guy","family":"Amit","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6367-2734","authenticated-orcid":false,"given":"Yisroel","family":"Mirsky","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Steve Adler. 2023. 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Retrieved from https:\/\/arxiv.org\/abs\/1705.07874"},{"issue":"13","key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"39779","DOI":"10.1007\/s11042-023-16706-x","article-title":"An exhaustive review of authentication, tamper detection with localization and recovery techniques for medical images","volume":"83","author":"Madhushree B.","year":"2024","unstructured":"B. Madhushree, H. B. Basanth Kumar, and H. R. Chennamma. 2024. An exhaustive review of authentication, tamper detection with localization and recovery techniques for medical images. 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Retrieved from https:\/\/arxiv.org\/abs\/2409.05585"},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","unstructured":"Louise Piecuch Jeremie Huet Antoine Frouin Antoine Nordez Anne-Sophie Boureau and Diana Mateus. 2025. Unsupervised anomaly detection on implicit shape representations for sarcopenia detection. arXiv:2502.09088. Retrieved from https:\/\/arxiv.org\/abs\/2502.09088","DOI":"10.1109\/ISBI60581.2025.10980714"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102475"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-23081-4"},{"key":"e_1_3_2_61_2","first-page":"8821","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ramesh Aditya","year":"2021","unstructured":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. 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