Uma Abordagem a DeepFake via Algoritmos de Aprendizagem Profunda
Resumo
Com o rápido avanço da inteligência artificial, ações de criação e manipulação de imagens e vídeos tem se tornado cada vez mais comum e acessível às pessoas. Como consequência, as deepfakes tem sido bastante utilizadas à serviço da desinformação e à disseminação de fake news. Considerando as deepfakes como um marco nessa nova era da informação, este artigo propõe a análise de três importantes algoritmos de aprendizagem profunda para a geração de deepfakes. Os resultados mostram que o Deepfacelab despende quase o dobro do tempo de processamento em relação ao FaceSwap e ao First Order Motion, porém apresenta uma melhor qualidade dos resultados gerados.
Referências
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Appel, M. and Prietzel, F. (2022). The detection of political deepfakes. Journal of Computer-Mediated Communication, 27(4):zmac008.
Bulat, A. and Tzimiropoulos, G. (2017). How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3d facial landmarks). In Proceedings of the IEEE international conference on computer vision, pages 1021–1030.
Chan, C., Ginosar, S., Zhou, T., and Efros, A. A. (2019). Everybody dance now. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5933–5942.
Deepfakes (2017). Faceswap. https://github.com/deepfakes/faceswap. Acessado: 03-04-2023.
Fagni, T., Falchi, F., Gambini, M., Martella, A., and Tesconi, M. (2021). Tweepfake: About detecting deepfake tweets. Plos one, 16(5):e0251415.
Iglovikov, V. I. and Shvets, A. A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. ArXiv, abs/1801.05746.
Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3):138–147.
Korshunov, P. and Marcel, S. (2018). Deepfakes: a new threat to face recognition? assessment and detection. arXiv preprint arXiv:1812.08685.
Maras, M.-H. and Alexandrou, A. (2019). Determining authenticity of video evidence in the age of artificial intelligence and in the wake of deepfake videos. The International Journal of Evidence & Proof, 23(3):255–262.
McCosker, A. (2022). Making sense of deepfakes: Socializing ai and building data literacy on github and youtube. New Media & Society, page 14614448221093943.
Mirsky, Y. and Lee, W. (2021). The creation and detection of deepfakes: A survey. ACM Computing Surveys (CSUR), 54(1):1–41.
Naruniec, J., Helminger, L., Schroers, C., and Weber, R. M. (2020). High-resolution neural face swapping for visual effects. In Computer Graphics Forum, volume 39, pages 173–184. Wiley Online Library.
Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, D. T., and Nahavandi, S. (2019). Deep learning for deepfakes creation and detection. arXiv preprint arXiv:1909.11573, 1:2.
Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Umé, C., Dpfks, M., Facenheim, C. S., RP, L., Jiang, J., et al. (2020). Deepfacelab: Integrated, flexible and extensible face-swapping framework. arXiv preprint arXiv:2005.05535.
Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nießner, M. (2018). Faceforensics: A large-scale video dataset for forgery detection in human faces.
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., and Sebe, N. (2019). First order motion model for image animation. Advances in Neural Information Processing Systems, 32.
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., and Ortega-Garcia, J. (2020). Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64:131–148.
Usukhbayar, B. and Homer, S. (2020). Deepfake videos: The future of entertainment. http://dx.doi.org/10.13140/RG.2.2.28924.62085.
Vaccari, C. and Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1):2056305120903408.
Yu, P., Xia, Z., Fei, J., and Lu, Y. (2021). A survey on deepfake video detection. Iet Biometrics, 10(6):607–624.
Zhu, B., Fang, H., Sui, Y., and Li, L. (2020). Deepfakes for medical video deidentification: Privacy protection and diagnostic information preservation. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pages 414–420.
Appel, M. and Prietzel, F. (2022). The detection of political deepfakes. Journal of Computer-Mediated Communication, 27(4):zmac008.
Bulat, A. and Tzimiropoulos, G. (2017). How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3d facial landmarks). In Proceedings of the IEEE international conference on computer vision, pages 1021–1030.
Chan, C., Ginosar, S., Zhou, T., and Efros, A. A. (2019). Everybody dance now. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5933–5942.
Deepfakes (2017). Faceswap. https://github.com/deepfakes/faceswap. Acessado: 03-04-2023.
Fagni, T., Falchi, F., Gambini, M., Martella, A., and Tesconi, M. (2021). Tweepfake: About detecting deepfake tweets. Plos one, 16(5):e0251415.
Iglovikov, V. I. and Shvets, A. A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. ArXiv, abs/1801.05746.
Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3):138–147.
Korshunov, P. and Marcel, S. (2018). Deepfakes: a new threat to face recognition? assessment and detection. arXiv preprint arXiv:1812.08685.
Maras, M.-H. and Alexandrou, A. (2019). Determining authenticity of video evidence in the age of artificial intelligence and in the wake of deepfake videos. The International Journal of Evidence & Proof, 23(3):255–262.
McCosker, A. (2022). Making sense of deepfakes: Socializing ai and building data literacy on github and youtube. New Media & Society, page 14614448221093943.
Mirsky, Y. and Lee, W. (2021). The creation and detection of deepfakes: A survey. ACM Computing Surveys (CSUR), 54(1):1–41.
Naruniec, J., Helminger, L., Schroers, C., and Weber, R. M. (2020). High-resolution neural face swapping for visual effects. In Computer Graphics Forum, volume 39, pages 173–184. Wiley Online Library.
Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, D. T., and Nahavandi, S. (2019). Deep learning for deepfakes creation and detection. arXiv preprint arXiv:1909.11573, 1:2.
Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Umé, C., Dpfks, M., Facenheim, C. S., RP, L., Jiang, J., et al. (2020). Deepfacelab: Integrated, flexible and extensible face-swapping framework. arXiv preprint arXiv:2005.05535.
Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nießner, M. (2018). Faceforensics: A large-scale video dataset for forgery detection in human faces.
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., and Sebe, N. (2019). First order motion model for image animation. Advances in Neural Information Processing Systems, 32.
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., and Ortega-Garcia, J. (2020). Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64:131–148.
Usukhbayar, B. and Homer, S. (2020). Deepfake videos: The future of entertainment. http://dx.doi.org/10.13140/RG.2.2.28924.62085.
Vaccari, C. and Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1):2056305120903408.
Yu, P., Xia, Z., Fei, J., and Lu, Y. (2021). A survey on deepfake video detection. Iet Biometrics, 10(6):607–624.
Zhu, B., Fang, H., Sui, Y., and Li, L. (2020). Deepfakes for medical video deidentification: Privacy protection and diagnostic information preservation. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pages 414–420.
Publicado
06/08/2023
Como Citar
RODRIGUES, Gustavo S.; SILVA, Carlos A..
Uma Abordagem a DeepFake via Algoritmos de Aprendizagem Profunda. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 10. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 21-28.
ISSN 2763-8766.
DOI: https://doi.org/10.5753/encompif.2023.230761.
