Design and Implementation of an AI/ML Framework for Identifying Face-Swapped Deepfake videos

Deep learning has revolutionized various complex tasks, including image interpretation, autonomous system control, and large-scale data analysis. However, its advancements have also facilitated the development of sophisticated tools capable of generating highly realistic yet fraudulent media content, known as deepfakes. These AI-generated images and videos can convincingly mimic real individuals, raising significant concerns regarding national security, democratic integrity, and personal privacy. Consequently, there is an urgent need for intelligent detection systems that can effectively identify and verify the authenticity of digital media. Such systems are crucial for distinguishing between genuine and manipulated content, ensuring the reliability of information, and preventing the dissemination of misleading visuals. This paper delves into the methodologies employed in creating prominent deepfakes and reviews the current literature on detection strategies. Furthermore, it discusses the inherent challenges posed by deepfake technologies and outlines prospective avenues for future research aimed at developing more robust and trustworthy detection mechanisms.