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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>\n            With the rise of the metaverse, the rapid advancement of Deepfakes technology has become closely intertwined. Within the metaverse, individuals exist in digital form and engage in interactions, transactions, and communications through virtual avatars. However, the development of Deepfakes technology has led to the proliferation of forged information disseminated under the guise of users\u2019 virtual identities, posing significant security risks to the metaverse. Hence, there is an urgent need to research and develop more robust methods for detecting deep forgeries to address these challenges. This article explores deepfake video detection by leveraging the spatiotemporal inconsistencies generated by deepfake generation techniques, thereby proposing the interactive spatiotemporal inconsistency learning and interactive fusion (ST-ILIF) detection method, which consists of phase-aware and sequence streams. The spatial inconsistencies exhibited in frames of deepfake videos are primarily attributed to variations in the structural information contained within the phase component of the Fourier domain. To mitigate the issue of overfitting the content information, a phase-aware stream is introduced to learn the spatial inconsistencies from the phase-based reconstructed frames. Additionally, considering that deepfake videos are generated frame by frame and lack temporal consistency between frames, a sequence stream is proposed to extract temporal inconsistency features from the spatiotemporal difference information between consecutive frames. Finally, through feature interaction and fusion of the two streams, the representation ability of intermediate and classification features is further enhanced. The proposed method, which was evaluated on four mainstream datasets, outperformed most existing methods, and extensive experimental results demonstrated its effectiveness in identifying deepfake videos. Our source code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/qff98\/Deepfake-Video-Detection\">https:\/\/github.com\/qff98\/Deepfake-Video-Detection<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3664654","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T11:08:51Z","timestamp":1715598531000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2789-2980","authenticated-orcid":false,"given":"Dengyong","family":"Zhang","sequence":"first","affiliation":[{"name":"Changsha University of Science and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2414-9307","authenticated-orcid":false,"given":"Wenjie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Changsha University of Science and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-0578","authenticated-orcid":false,"given":"Xin","family":"Liao","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3030-5589","authenticated-orcid":false,"given":"Feifan","family":"Qi","sequence":"additional","affiliation":[{"name":"Changsha University of Science and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2734-659X","authenticated-orcid":false,"given":"Gaobo","family":"Yang","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6581-4633","authenticated-orcid":false,"given":"Xiangling","family":"Ding","sequence":"additional","affiliation":[{"name":"Hunan University of Science and Technology, Xiangtan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Iryna Korshunova Wenzhe Shi Joni Dambre and Lucas Theis. 2017. 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