Deepfake Technology: Overview and Emerging Trends in Social Media

Abstract

In recent years, deepfake technology has garnered substantial attention as a rapidly evolving field at the intersection of artificial intelligence and multimedia. While deepfakes hold promise for applications in entertainment, education, and research, they also pose significant threats in areas such as misinformation, political manipulation, and fraud. This survey paper aims to provide a comprehensive understanding of deepfake technology, covering its technical foundations, particularly the use of Generative Adversarial Networks (GANs), and exploring its legal and ethical considerations. We examine current stateof-the-art detection techniques, existing datasets for deepfake research, and the associated challenges. Furthermore, the paper discusses countermeasures to mitigate the adverse effects of deepfakes and outlines future research directions. Case studies illustrate the real-world implications and societal impact of deepfakes, emphasizing the urgent need for robust solutions in this domain.

Keywords: Deepfake, GANs model, CNN, RNN, DeepfakeApp, SocialMedia