The Convergence of Deep Learning and DeepFake: A Study on AI-Generated Media Manipulation
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P104Keywords:
DeepFake, Deep Learning, Generative Adversarial Networks (GANs), Media Manipulation, Autoencoders, Face Swapping, AI EthicsAbstract
Through artificial intelligence and machine learning, especially deep learning, there has been a great change in the future of digital media. The key advancement that has attracted quite a significant level of controversy and consequence in duality is known as DeepFake technology. DeepFakes are generated by generative models, including GANs and autoencoders, to create realistic content that mimics real-life persons. The following paper investigates deep learning in relation to DeepFake technologies in terms of their background, development, usages, and impacts before February 2021. This paper is concerned with an introduction to the type of algorithms employed in DeepFakes, the entertainment, politics, and cyber-security fields in which DeepFakes have found application, as well as the counter-measures that have been put in place to address the problems associated with AI-generated media forgery. This paper makes a methodological contribution to the emerging research on DeepFakes by conducting empirical investigations, synthesizing from literature, and assessing models of DeepFakes to inform a given environment. However, the decision on ethical issues, regulatory requirements, and possible further research relating to AI media synthesis and detection is also discussed at the end of the paper
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