{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:44:13Z","timestamp":1767339853330,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T00:00:00Z","timestamp":1614297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,2,26]]},"DOI":"10.1145\/3457682.3457736","type":"proceedings-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T21:53:38Z","timestamp":1624312418000},"page":"356-360","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Deepfake Video Detection by Using Convolutional Gated Recurrent Unit"],"prefix":"10.1145","author":[{"given":"Yifeng","family":"Tu","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueming","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Retrieved","author":"Liu Ming-Yu","year":"2018","unstructured":"Ming-Yu Liu , Thomas Breuel , and Jan Kautz . 2018 . Unsupervised Image-to-Image Translation Networks. (July 2018) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1703.00848 Ming-Yu Liu, Thomas Breuel, and Jan Kautz. 2018. Unsupervised Image-to-Image Translation Networks. (July 2018). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1703.00848"},{"key":"e_1_3_2_1_2_1","volume-title":"Retrieved","author":"Agarwal Sakshi","year":"2020","unstructured":"Sakshi Agarwal and Lav R. Varshney . 2019. Limits of Deepfake Detection: A Robust Estimation Viewpoint. (May 2019) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1905.03493 Sakshi Agarwal and Lav R. Varshney. 2019. Limits of Deepfake Detection: A Robust Estimation Viewpoint. (May 2019). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1905.03493"},{"key":"e_1_3_2_1_3_1","volume-title":"Retrieved","author":"Mehta Ivan","year":"2019","unstructured":"Ivan Mehta . 2019 . A new study says nearly 96% of deepfake videos are porn. (October 2019) . Retrieved November 30, 2020 from https:\/\/thenextweb.com\/apps\/2019\/10\/07\/a-new-study-says-nearly-96-of-deepfake-videos-are-porn Ivan Mehta. 2019. A new study says nearly 96% of deepfake videos are porn. (October 2019). Retrieved November 30, 2020 from https:\/\/thenextweb.com\/apps\/2019\/10\/07\/a-new-study-says-nearly-96-of-deepfake-videos-are-porn"},{"key":"e_1_3_2_1_4_1","volume-title":"Retrieved","author":"Dolhansky Brian","year":"2019","unstructured":"Brian Dolhansky , Russ Howes , Ben Pflaum , Nicole Baram , and Cristian Canton Ferrer . 2019 . The Deepfake Detection Challenge (DFDC) Preview Dataset. (October 2019) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1910.08854 Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram, and Cristian Canton Ferrer. 2019. The Deepfake Detection Challenge (DFDC) Preview Dataset. (October 2019). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1910.08854"},{"key":"e_1_3_2_1_5_1","volume-title":"Retrieved","author":"Li Yuezun","year":"2019","unstructured":"Yuezun Li , Xin Y. ang, Pu Sun , Honggang Qi , and Siwei Lyu . 2019 . Celeb-DF (v2): A New Dataset for DeepFake Forensics. (November 2019) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/pdf\/1909.12962v3.pdf Yuezun Li, Xin Y. ang, Pu Sun, Honggang Qi, and Siwei Lyu. 2019. Celeb-DF (v2): A New Dataset for DeepFake Forensics. (November 2019). Retrieved November 30, 2020 from https:\/\/arxiv.org\/pdf\/1909.12962v3.pdf"},{"key":"e_1_3_2_1_6_1","first-page":"00009","article-title":"FaceForensics++: Learning to Detect Manipulated Facial Images.2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul","volume":"2019","author":"R\u00f6ssler A.","year":"2019","unstructured":"A. R\u00f6ssler , D. Cozzolino , L. Verdoliva , C. Riess , J. Thies and M. Niessner . 2019 . FaceForensics++: Learning to Detect Manipulated Facial Images.2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul , Korea (South),1-11.https:\/\/doi.org\/10.1109\/ICCV. 2019 . 00009 A. R\u00f6ssler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies and M. Niessner. 2019.FaceForensics++: Learning to Detect Manipulated Facial Images.2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South),1-11.https:\/\/doi.org\/10.1109\/ICCV.2019.00009","journal-title":"Korea (South),1-11.https:\/\/doi.org\/10.1109\/ICCV."},{"key":"e_1_3_2_1_7_1","volume-title":"Mesonet: a compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS) 1-7)","author":"I.","year":"2018","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. 2018, December . Mesonet: a compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS) 1-7) . IEEE. D. Afchar, V. Nozick, J. Yamagishi and I. Echizen. 2018 .MesoNet: a Compact Facial Video Forgery Detection Network. 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, Hong Kong , 1-7, https:\/\/doi.org\/10.1109\/WIFS.2018.8630761 Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. 2018, December. Mesonet: a compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS) 1-7). IEEE. D. Afchar, V. Nozick, J. Yamagishi and I. Echizen. 2018.MesoNet: a Compact Facial Video Forgery Detection Network. 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, Hong Kong, 1-7, https:\/\/doi.org\/10.1109\/WIFS.2018.8630761"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2012.2190402"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.31193\/ssap.01.9787509752807"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00116"},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition.IEEE","author":"Liu Wei","year":"2015","unstructured":"Szegedy, Christian, Wei Liu , Yangqing Jia , Pierre Sermanet , Scott Reed , Dragomir Anguelov , Dumitru Erhan , Vincent Vanhoucke , and Andrew Rabinovich . 2015 . Going deeper with convolutions . In Proceedings of the IEEE conference on computer vision and pattern recognition.IEEE , Boston, MA,1-9. https:\/\/doi.org\/10.1109\/CVPR. 2015.7298594 Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition.IEEE, Boston, MA,1-9. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_13_1","volume-title":"HI, 1800-1807","author":"Chollet F.","year":"2017","unstructured":"F. Chollet . 2017 . Xception: Deep Learning with Depthwise Separable Convolutions,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu , HI, 1800-1807 . https:\/\/doi.org\/10.1109\/CVPR.2017.195 F. Chollet. 2017. Xception: Deep Learning with Depthwise Separable Convolutions,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 1800-1807. https:\/\/doi.org\/10.1109\/CVPR.2017.195"},{"key":"e_1_3_2_1_14_1","first-page":"2307","article-title":"Capsule-forensics:Using Capsule Networks to Detect Forged Images and Videos.ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton","author":"Nguyen H. H.","year":"2019","unstructured":"H. H. Nguyen , J. Yamagishi and I. Echizen . 2019 . Capsule-forensics:Using Capsule Networks to Detect Forged Images and Videos.ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton , United Kingdom , 2307 - 2311 . https:\/\/doi.org\/10.1109\/ICASSP.2019.8682602 H. H. Nguyen, J. Yamagishi and I. Echizen. 2019. Capsule-forensics:Using Capsule Networks to Detect Forged Images and Videos.ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2307-2311. https:\/\/doi.org\/10.1109\/ICASSP.2019.8682602","journal-title":"United Kingdom"},{"key":"e_1_3_2_1_15_1","volume-title":"Retrieved","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015 . Very Deep Convolutional Networks for Large-Scale Image Recognition. (April 2015) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1409.1556 Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. (April 2015). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"e_1_3_2_1_16_1","first-page":"1","volume-title":"Deepfake Video Detection Using Recurrent Neural Networks. In 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","author":"G\u00fcera D.","year":"2018","unstructured":"D. G\u00fcera and E. J. Delp . 2018 . Deepfake Video Detection Using Recurrent Neural Networks. In 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) , Auckland, New Zealand , 1 - 6 . https:\/\/doi.org\/10.1109\/AVSS. 2018 .8639163 D. G\u00fcera and E. J. Delp. 2018. Deepfake Video Detection Using Recurrent Neural Networks. In 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand, 1-6. https:\/\/doi.org\/10.1109\/AVSS.2018.8639163"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.1994.8753425"},{"volume-title":"Long short term memory","author":"Hochreiter Sepp","key":"e_1_3_2_1_18_1","unstructured":"Sepp Hochreiter and Schmidhuber J\u00fcrgen . 1995. Long short term memory , M\u00fcnchen : Inst. f\u00fcr Informatik . Sepp Hochreiter and Schmidhuber J\u00fcrgen. 1995. Long short term memory, M\u00fcnchen: Inst. f\u00fcr Informatik."},{"key":"e_1_3_2_1_19_1","volume-title":"Retrieved","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho , Bart van Merrienboer , Dzmitry Bahdanau , and Yoshua Bengio . 2014 . On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. (October 2014) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1409.1259 Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. (October 2014). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1409.1259"},{"key":"e_1_3_2_1_20_1","unstructured":"Shi X. Chen Z. Wang H. Yeung D. Y. Wong W. K. & Woo W. C. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems 28 802-810.  Shi X. Chen Z. Wang H. Yeung D. Y. Wong W. K. & Woo W. C. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems 28 802-810."},{"key":"e_1_3_2_1_21_1","first-page":"1","article-title":"Learning to detect violent videos using convolutional long short-term memory.In 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","author":"Sudhakaran S.","year":"2017","unstructured":"S. Sudhakaran and O. Lanz . 2017 . Learning to detect violent videos using convolutional long short-term memory.In 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) , Lecce , 1 - 6 . https:\/\/doi.org\/10.1109\/AVSS.2017.8078468 S. Sudhakaran and O. Lanz. 2017. Learning to detect violent videos using convolutional long short-term memory.In 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, 1-6. https:\/\/doi.org\/10.1109\/AVSS.2017.8078468","journal-title":"Lecce"},{"key":"e_1_3_2_1_22_1","volume-title":"Retrieved","author":"Shi Xingjian","year":"2017","unstructured":"Xingjian Shi 2017 . Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. (October 2017) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1706.03458 Xingjian Shi 2017. Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. (October 2017). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1706.03458"},{"key":"e_1_3_2_1_23_1","first-page":"770","volume-title":"Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He K.","year":"2016","unstructured":"K. He , X. Zhang , S. Ren and J. Sun . 2016 . Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, NV , 770 - 778 . https:\/\/doi.org\/10.1109\/CVPR. 2016 .90 K. He, X. Zhang, S. Ren and J. Sun.2016. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 770-778. https:\/\/doi.org\/10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_24_1","volume-title":"Retrieved","author":"Korshunov Pavel","year":"2018","unstructured":"Pavel Korshunov and Sebastien Marcel . 2018 . DeepFakes: a New Threat to Face Recognition? Assessment and Detection. (December 2018) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1812.08685 Pavel Korshunov and Sebastien Marcel. 2018. DeepFakes: a New Threat to Face Recognition? Assessment and Detection. (December 2018). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1812.08685"},{"key":"e_1_3_2_1_25_1","volume-title":"Retrieved","author":"R\u00f6ssler Andreas","year":"2018","unstructured":"Andreas R\u00f6ssler , Davide Cozzolino , Luisa Verdoliva , Christian Riess , Justus Thies , and Matthias Nie\u00dfner . 2018 . FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces. (March 2018) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1803.09179 Andreas R\u00f6ssler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nie\u00dfner. 2018. FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces. (March 2018). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/1803.09179"},{"key":"e_1_3_2_1_26_1","volume-title":"Retrieved","author":"Jiang Liming","year":"2020","unstructured":"Liming Jiang , Wayne Wu , Ren Li , Chen Qian , and Chen Change Loy .2020. DeeperForensics-1.0 : A Large-Scale Dataset for Real-World Face Forgery Detection. (December 2020) . Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/2001.03024 Liming Jiang, Wayne Wu, Ren Li, Chen Qian, and Chen Change Loy.2020. DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection. (December 2020). Retrieved November 30, 2020 from https:\/\/arxiv.org\/abs\/2001.03024"}],"event":{"name":"ICMLC 2021: 2021 13th International Conference on Machine Learning and Computing","acronym":"ICMLC 2021","location":"Shenzhen China"},"container-title":["2021 13th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3457682.3457736","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3457682.3457736","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:08Z","timestamp":1750195688000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3457682.3457736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,26]]},"references-count":26,"alternative-id":["10.1145\/3457682.3457736","10.1145\/3457682"],"URL":"https:\/\/doi.org\/10.1145\/3457682.3457736","relation":{},"subject":[],"published":{"date-parts":[[2021,2,26]]},"assertion":[{"value":"2021-06-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}