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What is Generative Adversarial Networks (GAN)

Deep Natural Language Processing and AI Applications for Industry 5.0
They are the generative models using unsupervised learning task to automatically discover and learn patterns in input data to generate or output new examples that look like original.
Published in Chapter:
Recent Trends in Deepfake Detection
Kerenalli Sudarshana (GITAM School of Technology, Bengaluru, India) and Mylarareddy C. (GITAM School of Technology, Bengaluru, India)
DOI: 10.4018/978-1-7998-7728-8.ch001
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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Contemporary Imagetics and Post-Images in Digital Media Art: Inspirational Artists and Current Trends (1948-2020)
Machine learning framework in which two neural networks compete against each other to win within a gaming environment using a supervised learning pattern.
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Privacy and Other Legal Concerns in the Wake of Deepfake Technology: Comparative Study of India, US, and China
An advanced form of AI used for the creation of deepfakes. It involves the use of two neural networks (generator and discriminator) on previous data to compete against each other, thereby improving upon the output of each other and creating real looking morphed images and videos.
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