{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T03:32:24Z","timestamp":1775619144658,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["101073928"],"award-info":[{"award-number":["101073928"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Deepfake detection has become a critical issue due to the rise of synthetic media and its potential for misuse. In this paper, we propose a novel approach to deepfake detection by combining video frame analysis with facial microexpression features. The dual-branch fusion model utilizes a 3D ResNet18 for spatiotemporal feature extraction and a transformer model to capture microexpression patterns, which are difficult to replicate in manipulated content. We evaluate the model on the widely used FaceForensics++ (FF++) dataset and demonstrate that our approach outperforms existing state-of-the-art methods, achieving 99.81% accuracy and a perfect ROC-AUC score of 100%. The proposed method highlights the importance of integrating diverse data sources for deepfake detection, addressing some of the current limitations of existing systems.<\/jats:p>","DOI":"10.3390\/jimaging11070231","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:26:53Z","timestamp":1752229613000},"page":"231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Dual-Branch Fusion Model for Deepfake Detection Using Video Frames and Microexpression Features"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3371-569X","authenticated-orcid":false,"given":"Georgios","family":"Petmezas","sequence":"first","affiliation":[{"name":"Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2150-3446","authenticated-orcid":false,"given":"Vazgken","family":"Vanian","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece"}]},{"given":"Manuel Pastor","family":"Rufete","sequence":"additional","affiliation":[{"name":"Herta Security, 08037 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5886-0765","authenticated-orcid":false,"given":"Eleana E. I.","family":"Almaloglou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9649-9306","authenticated-orcid":false,"given":"Dimitris","family":"Zarpalas","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","article-title":"Deepfake Detection: A Systematic Literature Review","volume":"10","author":"Rana","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e1520","DOI":"10.1002\/widm.1520","article-title":"Deepfake detection using deep learning methods: A systematic and comprehensive review","volume":"14","author":"Heidari","year":"2023","journal-title":"Wiley Interdiscip. Rev. 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