Recent Trends in Deepfake Detection

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Author(s): Kerenalli Sudarshana (GITAM School of Technology, Bengaluru, India)and Mylarareddy C. (GITAM School of Technology, Bengaluru, India)
Copyright: 2021
Pages: 28
Source title: Deep Natural Language Processing and AI Applications for Industry 5.0
Source Author(s)/Editor(s): Poonam Tanwar (Manav Rachna International Institute of Research and Studies, India), Arti Saxena (Manav Rachna International Institute of Research and Studies, India)and C. Priya (Vels Institute of Science, Technology, and Advanced Studies, India)
DOI: 10.4018/978-1-7998-7728-8.ch001

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Natural Language Processing

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Abstract

Almost 59% of the world's population is on the internet, and in 2020, globally, there were more than 3.81 billion individual social network users. Eighty-six percent of the internet users were fooled to spread fake news. The advanced artificial intelligence (AI) algorithms can generate fake digital content that appears to be realistic. The generated content can deceive the users into believing it is real. These fabricated contents are termed deepfakes. The common category of deepfakes is video deepfakes. The deep learning techniques, such as auto-encoders and generative adversarial network (GAN), generate near realistic digital content. The content generated poses a serious threat to the multiple dimensions of human life and civil society. This chapter provides a comprehensive discussion on deepfake generation, detection techniques, deepfake generation tools, datasets, applications, and research trends.

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