{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T08:37:11Z","timestamp":1722933431334},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%.<\/jats:p>","DOI":"10.3390\/sym14050939","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T06:46:39Z","timestamp":1651819599000},"page":"939","source":"Crossref","is-referenced-by-count":17,"title":["Deepfake Video Detection Based on MesoNet with Preprocessing Module"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhiming","family":"Xia","sequence":"first","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4912-2132","authenticated-orcid":false,"given":"Tong","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China"},{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou Science and Technology Institute, Zhengzhou 450064, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9332-5258","authenticated-orcid":false,"given":"Ming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China"},{"name":"School of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou 310018, China"}]},{"given":"Xiaoshuai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"given":"Li","family":"Han","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"given":"Yunzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. 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