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These detection methods suffer from overfitting on the source dataset and do not perform well on cross-domain datasets which have different distributions from the source dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, which is a two-stage Deepfake detection method combining with transfer learning. Firstly, The CNN model pretrained on a third-party large-scale Deepfake dataset can be used to extract the more transferable feature vectors of Deepfake videos in the source and target domains. Secondly, these feature vectors are fed into the domain-adversarial neural network based on backpropagation (BP-DANN) for unsupervised domain adaptive training, where the videos in the source domain have real or fake labels, while the videos in the target domain are unlabelled. The experimental results indicate that the proposed method FeatureTransfer can effectively solve the overfitting problem in Deepfake detection and greatly improve the performance of cross-dataset evaluation.<\/jats:p>","DOI":"10.1155\/2021\/9942754","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T01:41:28Z","timestamp":1623116488000},"page":"1-8","source":"Crossref","is-referenced-by-count":16,"title":["FeatureTransfer: Unsupervised Domain Adaptation for Cross-Domain Deepfake Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9998-6433","authenticated-orcid":true,"given":"Baoying","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China"},{"name":"Shenzhen Key Laboratory of Media Security, Shenzhen, China"},{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7457-3691","authenticated-orcid":true,"given":"Shunquan","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China"},{"name":"Shenzhen Key Laboratory of Media Security, Shenzhen, China"},{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.11.022"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66046-8_28"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.11.007"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9010097"},{"key":"5","first-page":"1","article-title":"Byte-level object identification for forensic investigation of digital images","author":"A. 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