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8. Lyu S. Deepfake Detection: Current Challenges and Next Steps // IEEE International Conference on Multimedia & Expo Workshops (ICMEW). 2020. P. 1-6. DOI: 10.1109/ICMEW46912.2020.9105991
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13. FakeApp 2.2.0. URL: https://www.malavida.com/en/soft/fakeapp/ (дата обращения: 02.10.2021).
14. DeepFaceLab. The leading software for creating deepfakes. URL: https://github.com/iperov/DeepFaceLab (дата обращения: 02.10.2021).
15. DFaker. URL: https://github.com/dfaker/df (дата обращения: 02.10.2021).
16. DeepFaketf: Deepfake based on tensorflow. URL: https://github.com/StromWine/DeepFake_tf (дата обращения: 02.10.2021).
17. Thanh Thi Nguyen, Cuong M. Nguyen, Dung Tien Nguyen, Duc Thanh Nguyen and Saeid Naha- vandi. Deep Learning for Deepfakes Creation and Detection. URL: https://deepai.org/publication/deep-learning- for-deepfakes-creation-and-detection
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23. Irene Amerini, Roberto Caldelli. Exploiting prediction error inconsistencies through LSTM-based classifiers to detect deepfake videos.IH&MMSec '20: Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security. June 2020 P. 97-102. URL: https://doi.org/10.1145/3369412.3395070
24. Faceswap-GAN. URL: https://github.com/shaoanlu/faceswap-GAN (дата обращения: 02.10.2021).
25. VidTIMITAudio-VideoDataset. URL: http://conradsanderson.id.au/vidtimit/ (дата обращения: 02.10.2021).
26. Schroff F., Kalenichenko D., Philbin J. Facenet: A unified embedding for face recognition and clustering // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. P. 815-823.
27. Lip reading sentences in the wild / J.S. Chung, A. Senior, O. Vinyals, A. Zisserman // IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. P. 3444-3453.
28. Galbally J., Marcel S. Face anti-spoofing based on general image quality assessment // 22nd International Conference on Pattern Recognition - IEEE. 2014. P. 1173-1178.
29. Fast face-swap using convolutional neural networks / I. Korshunova, W. Shi, J. Dambre, L. Theis // Proceedings of the IEEE International Conference on Computer Vision. 2017. P. 3677-3685.
30. Zhang Y., Zheng L., Thing V.L. Automated face swapping and its detection // IEEE 2nd International Conference on Signal and Image Processing (ICSIP), IEEE. 2017. P. 15-19.
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32. Bai S. Growing random forest on deep convolutional neural networks for scene categorization // Expert Systems with Applications. 2017. No. 71. P. 279-287.
33. Siamese multi-layer perceptrons for dimensionality reduction and face identification / L. Zheng,
S. Duffner, K. Idrissi, C. Garcia, A. Baskurt // Multimedia Tools and Applications. 2016. No. 75 (9). P. 50555073.
34. On the generalization of GAN image forensics / X. Xuan, B. Peng, J. Dong, W. Wang // Preprint arXiv:1902.11153, 2019.
35. Yang P., Ni R., Zhao Y. Recapture image forensics based on Laplacian convolutional neural networks // International Workshop on Digital Watermarking. 2016. P. 119-128.
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T. Tan // Media Watermarking, Security and Forensics. 2015. Vol. 9409. P. 94090J.
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I. Echizen // IEEE International Workshop on Information Forensics and Security (WIFS) - IEEE. 2018. P. 1-7.
38. Densely connected convolutional networks / G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. P. 4700-4708.
39. Learning phrase representations using RNN encoder-decoder for statistical machine translation /
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40. Faceforensics++: Learning to detect manipulated facial images / A. Rossler, D. Cozzolino,
L. Verdoliva [et al.] // Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. P. 1-11.
41. Guera D., Delp E.J. Deepfake video detection using recurrent neural networks // 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) - IEEE. 2018. P. 1-6.
42. Hinton G.E., Krizhevsky A., Wang S.D. Transforming auto-encoders // International Conference on Artificial Neural Networks. Berlin; Heidelberg: Springer, 2011. P. 44-51.
43. Sabour S., Frosst N., Hinton G.E. Dynamic routing between capsules // Advances in Neural Information Processing Systems. 2017. P. 3856-3866.
44. Hasan H.R., Salah K. Combating deepfake videos using blockchain and smart contracts // IEEE Access. 2019. No. 7. P. 41596-41606.
45. IPFS powers the Distributed Web. URL: https://ipfs.io/ (дата обращения: 03.10.2021).
46. Довгаль В. А., Довгаль Д.В. Обнаружение и предотвращение атаки «злоумышленник в середине» в туманном слое роя дронов // Вестник Адыгейского государственного университета. Сер.: Естественно-математические и технические науки. 2020. Вып. 2 (261). С. 53-59. URL: http://vestnik.adygnet.ru
47. Chesney R., Citron D.K. (2018, October 16). Disinformation on steroids: The threat of deepfakes. URL: https://www.cfr.org/report/deep-fake-disinformation-steroids (дата обращения: 03.10.2021).
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33. Siamese multi-layer perceptrons for dimensionality reduction and face identification / L. Zheng,
S. Duffner, K. Idrissi, C. Garcia, A. Baskurt // Multimedia Tools and Applications. 2016. No. 75 (9). P. 50555073.
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36. Deep learning for steganalysis via convolutional neural networks / Y. Qian, J. Dong, W. Wang,
T. Tan // Media Watermarking, Security and Forensics. 2015. Vol. 9409. P. 94090J.
37. MesoNet: a compact facial video forgery detection network / D. Afchar, V. Nozick, J. Yamagishi, I. Echizen // IEEE International Workshop on Information Forensics and Security (WIFS) - IEEE. 2018. P. 1-7.
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