The rise of deepfake technology, powered by advancements in Artificial Intelligence (AI), has introduced significant challenges to digital media security. Deepfakes are synthetic media—such as videos, images, or audio—that are generated by deep learning techniques, primarily using Generative Adversarial Networks (GANs). While this technology has unlocked creative potential in entertainment, gaming, and virtual reality, it also presents critical ethical, legal, and security risks, including misinformation, identity theft, and manipulation of public opinion. This paper provides an in-depth review of AI-driven deepfake detection methods, highlighting the latest developments in deep learning architectures, hybrid models, statistical approaches, and forensic techniques. It covers a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer-based approaches, all of which are employed to identify subtle inconsistencies in manipulated media. Additionally, the paper assesses the performance of these models using standard evaluation metrics, such as accuracy, precision, recall, and Area Under Curve (AUC), while drawing comparisons across well-known benchmark datasets like FaceForensics++ and Celeb-DF. Despite the promising advances in detection capabilities, the paper also highlights several challenges, including the generalization of models to new manipulation techniques, vulnerability to adversarial attacks, and the computational resources required for real-world deployment. The review identifies key gaps in current research and outlines future directions, emphasizing the need for more robust, lightweight, and interpretable models. It also calls for interdisciplinary efforts to develop effective policy frameworks for regulating deepfake technologies and ensuring digital media integrity.

Keywords: Artificial Intelligence, GAN,Misinformation, Deepfake Detection, Video Forensics, Face Forgery, Neural Networks