Generative Adversarial Networks (GANs) have transformed the way of content creation. It has introduced a way where two neural networks work against each other to generate highly realistic content like images, videos, and audio on various social media platforms.
This paper focuses on the trends, ethical challenges, and prospects of deepfakes, which are a result of GAN technology. In the last few years, deepfakes have been further developed, driven by generative models and constant data advancements. It is a digital media forgery technique that can not only manipulate fake face content, but also generate it. This is making it difficult for users to differentiate between real and fake content. Although multiple studies have been conducted on deepfake detection, it is currently lagging behind deepfake generation, underscoring the need for comprehensive surveys on deepfake generation. The objective of this empirical study is to explore the growing use of deepfakes and associated risks and promote ways of deepfake detection. In this research, Clifford G. Christians’ Media Ethics Theory of Global Justice and Uses and Gratification Theory will be used to examine the issue of deepfakes.
The researcher analyzes the types of deepfakes and the factors driving their proliferation, understanding deepfake detection methods, understanding the current challenges of deepfakes, and the developing trends through a mixed-method approach integrating a youth survey and literature review, among the youth in Delhi. The sample includes 384 participants aged between 18 and 24 years.