Abstract
Deepfake algorithms are one of the most recent albeit controversial developments in Artificial Intelligence, because they use Machine Learning to generate fake yet realistic content (e.g., images, videos, audio, and text) based on an input dataset. For instance, they can accurately superimpose the face of an individual over the body an actor in a destination video (i.e., face swap), or exactly reproduce the voice of a person and speak a given text. As a result, many are concerned with the potential risks in terms of cybersecurity. Although most focused on the malicious applications of this technology, in this paper we propose a system for using deepfakes for beneficial purposes. We describe the potential use and benefits of our proposal and we discuss its implications in terms of human factors, security risks, and ethical aspects.
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References
Bevilacqua, V., Carnimeo, L., Brunetti, A., De Pace, A., Galeandro, P., Trotta, G.F., Caporusso, N., Marino, F., Alberotanza, V., Scardapane, A.: Synthesis of a neural network classifier for hepatocellular carcinoma grading based on triphasic ct images. In: International Conference on Recent Trends in Image Processing and Pattern Recognition, pp. 356–368. Springer, Singapore, December 2016
Bevilacqua, V., Trotta, G.F., Brunetti, A., Caporusso, N., Loconsole, C., Cascarano, G.D., Catino, F., Cozzoli, P., Delfine, G., Mastronardi, A., Di Candia, A.: A comprehensive approach for physical rehabilitation assessment in multiple sclerosis patients based on gait analysis. In: International Conference on Applied Human Factors and Ergonomics, pp. 119–128. Springer, Cham, July 2017
Caporusso, N., Helms, T., Zhang, P.: A meta-language approach for machine learning. In: International Conference on Applied Human Factors and Ergonomics, pp. 192–201. Springer, Cham, July 2019
Caporusso, N., Zhang, K., Carlson, G., Jachetta, D., Patchin, D., Romeiser, S., Vaughn, N., Walters, A.: User discrimination of content produced by generative adversarial networks. In: International Conference on Human Interaction and Emerging Technologies, pp. 725–730. Springer, Cham, August 2019
Maras, M.H., Alexandrou, A.: Determining authenticity of video evidence in the age of artificial intelligence and in the wake of Deepfake videos. Int. J. Evid. Proof 23(3), 255–262 (2019)
Chesney, R., Citron, D.K.: Deep fakes: a looming challenge for privacy, democracy, and national security (2018)
Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K., Li, H.: Protecting world leaders against deep fakes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–45, June 2019
Ajao, O., Bhowmik, D., Zargari, S.: Fake news identification on twitter with hybrid CNN and RNN models. In: Proceedings of the 9th International Conference on Social Media and Society, pp. 226–230, July 2018
Harris, D.: Deepfakes: false pornography is here and the law cannot protect you. Duke L. Tech. Rev. 17, 99 (2018)
Silbey, J., Hartzog, W.: The upside of deep fakes. Md. L. Rev. 78, 960 (2018)
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Caporusso, N. (2021). Deepfakes for the Good: A Beneficial Application of Contentious Artificial Intelligence Technology. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_33
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DOI: https://doi.org/10.1007/978-3-030-51328-3_33
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