{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:54:15Z","timestamp":1774968855206,"version":"3.50.1"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Face morphing attacks have become increasingly complex, and existing methods exhibit certain limitations in capturing fine-grained texture and detail changes. To overcome these limitation, in this study, a detection method based on high-frequency features and progressive enhancement learning was proposed. Specifically, in this method, first, high-frequency information are extracted from the three color channels of the image to accurately capture the details and texture changes. Next, a progressive enhancement learning framework was designed to fuse high-frequency information with RGB information. This framework includes self-enhancement and interactive-enhancement modules that progressively enhance features to capture subtle morphing traces. Experiments conducted on the standard database and compared with nine classical technologies revealed that the proposed approach achieved excellent performance.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1182375","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T04:34:20Z","timestamp":1685939660000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Face morphing attack detection based on high-frequency features and progressive enhancement learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Cheng-kun","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong-chao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-ling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"B1","author":"Biometrics","year":"2016","journal-title":"ISO\/IEC 30107-3-2017, Information technology-Biometric Presentation Attack Detection\u2014Part 3: Testing and reporting (First Edition)."},{"key":"B2","doi-asserted-by":"publisher","first-page":"3560","DOI":"10.1109\/WACV48630.2021.00360","article-title":"\u201cAttentional feature fusion,\u201d","author":"Dai","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"},{"key":"B3","first-page":"1","article-title":"\u201cPRNU variance analysis for morphed face image detection,\u201d","volume-title":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","author":"Debiasi","year":""},{"key":"B4","first-page":"1","article-title":"\u201cPRNU-based detection of morphed face images,\u201d","volume-title":"2018 International Workshop on Biometrics and Forensics (IWBF)","author":"Debiasi","year":""},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS.2014.6996240","article-title":"\u201cThe magic passport,\u201d","author":"Ferrara","year":"2014","journal-title":"IEEE International Joint Conference on Biometrics"},{"key":"B6","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1609\/aaai.v36i1.19954","article-title":"Exploiting fine-grained face forgery clues via progressive enhancement learning","volume":"36","author":"Gu","year":"2022","journal-title":"Proceedings of the AAAI Conf. 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