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             [4]   Matthias Nießner and Shivangi Aneja. Few-shot and   [10]   www.github.com/deepfakes/faceswap
                  generalized  zero-shot  transfer  for  detecting  facial   DeepFakesAccessed April 24, 2021.
                  forgeries.2020; arXiv preprint arXiv:2006.11863.
                                                               [11]   Li-Jia Li, Kai Li, Li Fei-Fei, Richard Socher, Wei Dong,
             [5]   Mario Döbler, Bin Yang, Felix Wiewel, Andre Bühler,   and  Jia  Deng.  An  extensive  hierarchical  image
                  and Alexander Bartler. Mt3: Self-supervised test-time   database is called Imagenet. 2009; in CVPR.
                  adaptation  using  meta  test-time  training.  Preprint   [12]
                  arXiv:2103.16201, 2021 and arXiv.                  The issue of deepfake detection. This link will take
                                                                     you to the deepfake-detection-challenge on Kaggle.
             [6]   Ngoc-Trung Tran, Ngai-Man Cheung, and Keshigeyan   Accessed April 24, 2021.
                  Chandrasegaran.  A  more  thorough  examination  of   [13]
                  fourier spectrum inconsistencies for the detection of   Baining  Guo,  Xiaoyi  Dong,  Jianmin  Bao,  Dongdong
                  CNN-generated images.                              Chen,  Ting  Zhang,  Weiming  Zhang,  Nenghai  Yu,
                                                                     DongChen,  and  FangWen  [5].  utilizing  an  identity
             [7]   Xiaoguang Han, Xiaoqing Liu, Jiongcheng Li, Yizhou   consistency  transformer  to  safeguard  celebrities.
                  Yu,   and   Chaoqi   Chen.   compound   domain     2022, in CVPR.
                  generalization by the encoding of meta-knowledge.   [14]   Freddie  Witherden,  Karan  Shah,  and  Tarik  Dzanic.
                  CVPR, 2022.
                                                                     Fourier spectrum differences in images produced by
             [8]   Yizhou  Yu,  Yue  Huang,  Gangming  Zhao,  Feng  Liu,   deep networks. 2020's NeurIPS.
                  Luyao Tang, and Chaoqi Chen. Mix and reason: Using   [15]   FaceSwap.   Accessed   April   24,   2021.
                  data mixing to provide domain generalization while   www.github.com/MarekKowalski/FaceSwap.
                  reasoning over semantic topology. 2022, in NeurIPS.
                  Jibing Song, Liang Chen, Yong Zhang, Lingqiao Liu, and   [16]   Sergey  Levine,  Pieter  Abbeel,  and  Chelsea  Finn.
                  Jue  Wang  [3].  Self-supervised  learning  of  an   Model-independent  meta-learning  for  rapid  deep
                  adversarial  example:  Moving  toward  sound       network adaptation. ICML, 2017.
                  generalizations for the detection of deepfakes. CVPR,   [17]   Jan Kodovsky and Jessica Fridrich. Rich models for
                  2022.
                                                                     digital image steganalysis. IEEE TIFS, 7(3), 2012, 868–
             [9]   Bingbing  Ni,  Yanhao  Ge,  Xuanhong  Chen,  and   882.
                  Renwang Chen. Simswap: An effective framework for   [18]
                  face  switching  in  high  fidelity.  In  2020,  ACM  MM.    Praveer Singh, Nikos Komodakis, and Pyros Gidaris.
                  Chollet, François [5]. Deep learning using depthwise   Predicting picture rotations allows for unsupervised
                  separable  convolutions  is  called  Xception.  2017's   representation learning. In 2018, the arXiv preprint
                  CVPR.          Deepfaked          detection.       arXiv:1803.07728.
                  https://ai.googleblog.com/2019/09/contributing-
                  data-to-deepfaked etection.html












































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