{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:02:35Z","timestamp":1781269355832,"version":"3.54.1"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Aust Endodontic J"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study evaluated how reliably humans can detect digital manipulations in dental radiographs and introduced an unsupervised, artefact\u2010aware artificial intelligence (AI) system to identify subtle, visually imperceptible changes. A dataset of 384 anonymised periapical radiographs was used, with equal numbers of original and synthetically altered images. Manipulations included copy\u2013move, splicing, inpainting and lesion modification using clinically relevant techniques. The AI framework combined Error Level Analysis with a probabilistic Gaussian Mixture Model and included artefact suppression layers to reduce false positives from metallic restorations and high\u2010contrast anatomical structures. It generated pixel\u2010level probability maps and classified images based on percentile thresholds. Two experienced endodontists independently evaluated the images, blinded to dataset composition, and agreement was measured using Cohen's Kappa. The AI system achieved 98% sensitivity, specificity and accuracy with near\u2010perfect agreement. In contrast, human observers showed poor performance, indicating AI offers a far more reliable solution.<\/jats:p>","DOI":"10.1111\/aej.70096","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:39:59Z","timestamp":1781267999000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond the Naked Eye: Automated Detection of Digital Manipulation in Dental Radiographs Using Probabilistic Detection Model"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2697-8907","authenticated-orcid":false,"given":"Ay\u015fe Tu\u011fba","family":"Eminsoy Avc\u0131","sequence":"first","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry Erciyes University  Kayseri T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9290-5661","authenticated-orcid":false,"given":"Yakup","family":"\u00dcst\u00fcn","sequence":"additional","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry Erciyes University  Kayseri T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-1551","authenticated-orcid":false,"given":"Tu\u011frul","family":"Aslan","sequence":"additional","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry Erciyes University  Kayseri T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0852-8136","authenticated-orcid":false,"given":"Halime","family":"\u00c7etiner","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry Erciyes University  Kayseri T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4689-7020","authenticated-orcid":false,"given":"Kaan","family":"Sa\u011fta\u015f","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering Sivas Cumhuriyet University  Sivas T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"issue":"4","key":"e_1_2_13_2_1","doi-asserted-by":"crossref","first-page":"e103","DOI":"10.1016\/j.radi.2017.05.003","article-title":"Detecting the Manipulation of Digital Clinical Records in Dental Practice","volume":"23","author":"D\u00edaz\u2010Flores\u2010Garc\u00eda V.","year":"2017","journal-title":"Radiography"},{"issue":"2","key":"e_1_2_13_3_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.4103\/jfo.jfds_16_18","article-title":"Digital Image Fraudulence: A Curse to Forensic Odontology","volume":"10","author":"Karyakarte G.","year":"2018","journal-title":"Journal of Forensic Dental Sciences"},{"key":"e_1_2_13_4_1","first-page":"1","article-title":"MemAU\u2010Net: Memory\u2010Enhanced Attention U\u2010Net for Medical Image Forgery Localization","volume":"2023","author":"Wang N.","year":"2023","journal-title":"International Joint Conference on Neural Networks (IJCNN); IEEE"},{"issue":"1","key":"e_1_2_13_5_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/sj.dmfr.4600204","article-title":"Can Dentists Recognize Manipulated Digital Radiographs?","volume":"26","author":"Visser H.","year":"1997","journal-title":"Dento Maxillo Facial Radiology"},{"key":"e_1_2_13_6_1","first-page":"1","article-title":"IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"2016","author":"Rao Y.","year":"2016","journal-title":"IEEE"},{"issue":"3","key":"e_1_2_13_7_1","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s11263-022-01617-5","article-title":"Learning Jpeg Compression Artifacts for Image Manipulation Detection and Localization","volume":"130","author":"Kwon M.\u2010J.","year":"2022","journal-title":"International Journal of Computer Vision"},{"key":"e_1_2_13_8_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110094","article-title":"Generative Adversarial Networks in Medical Image Reconstruction: A Systematic Literature Review","volume":"191","author":"Hussain J.","year":"2025","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_2_13_9_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109248","article-title":"MITS\u2010GAN: Safeguarding Medical Imaging From Tampering With Generative Adversarial Networks","volume":"183","author":"Pasqualino G.","year":"2024","journal-title":"Computers in Biology and Medicine"},{"issue":"5","key":"e_1_2_13_10_1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.joen.2008.01.019","article-title":"Fraudulent Use of Digital Radiography: Methods to Detect and Protect Digital Radiographs","volume":"34","author":"Calberson F. 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An Examination of the Effects of Meta\u2010Cognitive Myopia and Truth Bias on Mock Jurors and Judges","volume":"15","author":"Pantazi M.","year":"2020","journal-title":"Judgment and Decision Making"},{"issue":"12","key":"e_1_2_13_13_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3731243","article-title":"Unravelling Digital Forgeries: A Systematic Survey on Image Manipulation Detection and Localization","volume":"57","author":"Kadha V.","year":"2025","journal-title":"ACM Computing Surveys"},{"issue":"12","key":"e_1_2_13_14_1","doi-asserted-by":"crossref","first-page":"17521","DOI":"10.1007\/s11042-022-13797-w","article-title":"Image Forgery Detection: A Survey of Recent Deep\u2010Learning Approaches","volume":"82","author":"Zanardelli M.","year":"2023","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"e_1_2_13_15_1","doi-asserted-by":"crossref","first-page":"19683","DOI":"10.48084\/etasr.9593","article-title":"Enhanced Image Tampering Detection Using Error Level Analysis and CNN","volume":"15","author":"Gorle R.","year":"2025","journal-title":"Engineering, Technology & Applied Science Research"},{"key":"e_1_2_13_16_1","first-page":"1","article-title":"Radiographic Artifacts in the Diagnosis of Dental Caries: Systematic Review With Meta\u2010Analysis","volume":"2025","author":"Dioguardi M.","year":"2025","journal-title":"Oral Radiology"},{"key":"e_1_2_13_17_1","first-page":"626","volume-title":"International Conference on Medical Image Computing and Computer\u2010Assisted Intervention","author":"Li S.","year":"2025"},{"key":"e_1_2_13_18_1","first-page":"1","article-title":"Detection of GAN\u2010Manipulated Medical Images Through Deep Learning Techniques","volume":"2024","author":"Aruna S.","year":"2024","journal-title":"International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE)"},{"issue":"2","key":"e_1_2_13_19_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.35784\/acs_7054","article-title":"A Comprehensive Review of Deepfakes in Medical Imaging: Ethical Concerns, Detection Techniques and Future Directions","volume":"21","author":"Pradepan P.","year":"2025","journal-title":"Applied Computer Science"},{"issue":"7","key":"e_1_2_13_20_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0303332","article-title":"CFDMI\u2010SEC: An Optimal Model for Copy\u2010Move Forgery Detection of Medical Image Using SIFT, EOM and CHM","volume":"19","author":"Amiri E.","year":"2024","journal-title":"PLoS One"},{"issue":"2","key":"e_1_2_13_21_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MSP.2008.931079","article-title":"Image Forgery Detection","volume":"26","author":"Farid H.","year":"2009","journal-title":"IEEE Signal Processing Magazine"},{"key":"e_1_2_13_22_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/ACCESS.2013.2260814","article-title":"Information Forensics: An Overview of the First Decade","volume":"1","author":"Stamm M. 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