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Honggu Liu Xiaodan Li Wenbo Zhou Yuefeng Chen Yuan He Hui Xue Weiming Zhang and Nenghai Yu. 2021a. Spatial-phase shallow learning: rethinking face forgery detection in frequency domain. In CVPR. 772--781."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Ze Liu Yutong Lin Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin and Baining Guo. 2021b. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV. 10012--10022. Ze Liu Yutong Lin Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin and Baining Guo. 2021b. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV. 10012--10022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Yongyi Lu Yu-Wing Tai and Chi-Keung Tang. 2018. Attribute-Guided Face Generation Using Conditional CycleGAN. In ECCV. 293--308. Yongyi Lu Yu-Wing Tai and Chi-Keung Tang. 2018. Attribute-Guided Face Generation Using Conditional CycleGAN. 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In CVPRW. 80--87."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073640"},{"key":"e_1_3_2_2_42_1","volume-title":"Michael Zollh\u00f6 fer, and Matthias Nie\u00dfner","author":"Thies Justus","year":"2019","unstructured":"Justus Thies , Michael Zollh\u00f6 fer, and Matthias Nie\u00dfner . 2019 . Deferred neural rendering: image synthesis using neural textures. ACM TOG , Vol. 38 , 4 (2019), 66:1--12. Justus Thies, Michael Zollh\u00f6 fer, and Matthias Nie\u00dfner. 2019. Deferred neural rendering: image synthesis using neural textures. ACM TOG, Vol. 38, 4 (2019), 66:1--12."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Justus Thies Michael Zollhofer Marc Stamminger Christian Theobalt and Matthias Nie\u00dfner. 2016. Face2face: Real-time face capture and reenactment of rgb videos. In CVPR. 2387--2395. Justus Thies Michael Zollhofer Marc Stamminger Christian Theobalt and Matthias Nie\u00dfner. 2016. Face2face: Real-time face capture and reenactment of rgb videos. In CVPR. 2387--2395.","DOI":"10.1109\/CVPR.2016.262"},{"key":"e_1_3_2_2_44_1","volume-title":"DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance. In ICPR Workshops. 442--456","author":"Tolosana Rub\u00e9n","year":"2020","unstructured":"Rub\u00e9n Tolosana , Sergio Romero-Tapiador , Julian Fi\u00e9rrez , and Rub\u00e9n Vera-Rodriguez . 2020 . DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance. In ICPR Workshops. 442--456 . Rub\u00e9n Tolosana, Sergio Romero-Tapiador, Julian Fi\u00e9rrez, and Rub\u00e9n Vera-Rodriguez. 2020. DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance. In ICPR Workshops. 442--456."},{"key":"e_1_3_2_2_45_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones etal 2017. Attention is all you need. In NeurIPS. 5998--6008. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones et al. 2017. Attention is all you need. In NeurIPS. 5998--6008."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Sheng-Yu Wang Oliver Wang Richard Zhang Andrew Owens and Alexei A Efros. 2020. CNN-generated images are surprisingly easy to spot... for now. In CVPR. 8695--8704. Sheng-Yu Wang Oliver Wang Richard Zhang Andrew Owens and Alexei A Efros. 2020. CNN-generated images are surprisingly easy to spot... for now. In CVPR. 8695--8704.","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo and Ling Shao. 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In ICCV. 568--578. Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo and Ling Shao. 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In ICCV. 568--578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"e_1_3_2_2_48_1","volume-title":"Deepfake Video Detection Using Convolutional Vision Transformer. arXiv preprint arXiv:2102.11126","author":"Wodajo Deressa","year":"2021","unstructured":"Deressa Wodajo and Solomon Atnafu . 2021. Deepfake Video Detection Using Convolutional Vision Transformer. arXiv preprint arXiv:2102.11126 ( 2021 ). Deressa Wodajo and Solomon Atnafu. 2021. Deepfake Video Detection Using Convolutional Vision Transformer. arXiv preprint arXiv:2102.11126 (2021)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Xin Yang Yuezun Li and Siwei Lyu. 2019. Exposing Deep Fakes Using Inconsistent Head Poses. In ICASSP. 8261--8265. Xin Yang Yuezun Li and Siwei Lyu. 2019. Exposing Deep Fakes Using Inconsistent Head Poses. In ICASSP. 8261--8265.","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Daichi Zhang Chenyu Li Fanzhao Lin Dan Zeng and Shiming Ge. 2021. Detecting Deepfake Videos with Temporal Dropout 3DCNN. In IJCAI. 1288--1294. Daichi Zhang Chenyu Li Fanzhao Lin Dan Zeng and Shiming Ge. 2021. Detecting Deepfake Videos with Temporal Dropout 3DCNN. In IJCAI. 1288--1294.","DOI":"10.24963\/ijcai.2021\/178"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Hanqing Zhao Wenbo Zhou Dongdong Chen Tianyi Wei Weiming Zhang and Nenghai Yu. 2021. Multi-attentional deepfake detection. In CVPR. 2185--2194. Hanqing Zhao Wenbo Zhou Dongdong Chen Tianyi Wei Weiming Zhang and Nenghai Yu. 2021. Multi-attentional deepfake detection. In CVPR. 2185--2194.","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"e_1_3_2_2_52_1","volume-title":"Davis","author":"Zhou Peng","year":"2017","unstructured":"Peng Zhou , Xintong Han , Vlad I. Morariu , and Larry S . Davis . 2017 . Two-Stream Neural Networks for Tampered Face Detection. In CVPRW. 1831--1839. Peng Zhou, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2017. Two-Stream Neural Networks for Tampered Face Detection. In CVPRW. 1831--1839."},{"key":"e_1_3_2_2_53_1","unstructured":"Xiangyu Zhu Hao Wang Hongyan Fei Zhen Lei and Stan Z Li. 2021. Face Forgery Detection by 3D Decomposition. In CVPR. 2929--2939. Xiangyu Zhu Hao Wang Hongyan Fei Zhen Lei and Stan Z Li. 2021. Face Forgery Detection by 3D Decomposition. 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