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Previous researches have made effort to address this problem by various schemes to extract visual artifacts of non-pristine frames or discrepancy between real and fake videos, where the patch-based approaches are shown to be promising but mostly used in frame-level prediction. In this paper, we propose a method that leverages comprehensive consistency learning in both spatial and temporal relation with patch-based feature extraction. Extensive experiments on multiple datasets demonstrate the effectiveness and robustness of our approach by combines all consistency cue together.\n<\/jats:p>","DOI":"10.1007\/978-981-19-8285-9_11","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T20:02:48Z","timestamp":1670616168000},"page":"151-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Deepfake Video Detection with\u00a0Comprehensive Self-consistency Learning"],"prefix":"10.1007","author":[{"given":"Heng","family":"Bao","sequence":"first","affiliation":[]},{"given":"Lirui","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Jiazhi","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xunxun","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: MesoNet: a compact facial video forgery detection network. 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