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Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high\u2010quality faces under intra\u2010dataset scenarios, they often overfit manipulation\u2010specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual\u2010branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high\u2010level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer\u2010based branch can track long\u2010range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency\u2010aware module (MFAM) is developed and further applied to the CNN\u2010based branch to extract complementary frequency\u2010aware clues. Besides, we incorporate a collaboration strategy involving cross\u2010entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.<\/jats:p>","DOI":"10.1049\/2024\/6523854","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T18:50:07Z","timestamp":1707159007000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Face Forgery Detection with Long\u2010Range Noise Features and Multilevel Frequency\u2010Aware Clues"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9548-2721","authenticated-orcid":false,"given":"Yi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liwen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-4246","authenticated-orcid":false,"given":"Shaowen","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3097-0721","authenticated-orcid":false,"given":"Qian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1049\/bme2.12031"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109778"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3002101"},{"key":"e_1_2_9_4_2","doi-asserted-by":"crossref","unstructured":"AfcharD. 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