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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            The widespread dissemination of Deepfake in social networks has posed serious security risks, thus necessitating the development of an effective Deepfake detection technique. Currently, video-based detectors have not been explored as extensively as image-based detectors. Most existing video-based methods only consider temporal features without combining spatial features, and do not mine deeper-level subtle forgeries, resulting in limited detection performance. In this paper, a novel\n            <jats:bold>spatiotemporal trident network (STN)<\/jats:bold>\n            is proposed to detect both spatial and temporal inconsistencies of Deepfake videos. Since there is a large amount of redundant information in Deepfake video frames, we introduce\n            <jats:bold>convolutional block attention module (CBAM)<\/jats:bold>\n            on the basis of the I3D network and optimize the structure to make the network better focus on the meaningful information of the input video. Aiming at the defects in the deeper-level subtle forgeries, we designed three\n            <jats:bold>feature extraction modules (FEMs)<\/jats:bold>\n            of RGB, optical flow, and noise to further extract deeper video frame information. Extensive experiments on several well-known datasets demonstrate that our method has promising performance, surpassing several state-of-the-art Deepfake video detection methods.\n          <\/jats:p>","DOI":"10.1145\/3623639","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T12:18:28Z","timestamp":1694607508000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Detecting Deepfake Videos using Spatiotemporal Trident Network"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6153-3722","authenticated-orcid":false,"given":"Kaihan","family":"Lin","sequence":"first","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9997-1509","authenticated-orcid":false,"given":"Weihong","family":"Han","sequence":"additional","affiliation":[{"name":"Department of New Networks, Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6381-1984","authenticated-orcid":false,"given":"Shudong","family":"Li","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University; Department of New Networks, Peng Cheng Laboratory, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7546-852X","authenticated-orcid":false,"given":"Zhaoquan","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology(Shenzhen); Department of New Networks, Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-2002","authenticated-orcid":false,"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7050-4437","authenticated-orcid":false,"given":"Yangyang","family":"Mei","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"2018 IEEE International Workshop on Information Forensics and Security (WIFS\u201918)","author":"Afchar Darius","year":"2018","unstructured":"Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 2018. 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