Paper Title
A Comprehensive Survey on Deepake Technology: Evolution, Applications, and Detection Strategies

Abstract
Deepfake technology, driven by advancements in artificial intelligenceparticularly Generative Adversarial Networks (GANs)has rapidly evolved to produce hyper-realistic synthetic media, raising significant societal, ethical, and security concerns. This survey paper provides a comprehensive overview of the evolution of deepfake generation techniques, from traditional visual effects to sophisticated AI-driven methods, highlighting the increasing accessibility and realism of manipulated content. It explores the origins and early development of deepfakes, their widespread applications in fields like entertainment, education, accessibility, and law enforcement, as well as the growing risks they pose in misinformation, privacy invasion, and digital fraud. The core focus lies in evaluating recent deepfake detection approaches, including biometric-based methods, attention mechanisms, forensic similarity analysis, and benchmark datasets such as FaceForensics++, Celeb-DF, DFDC, and WildDeepfake. We analyze the strengths, limitations, and generalization capabilities of various detection models, emphasizing challenges like dataset bias, evolving generation techniques, and real-world adaptability. Furthermore, the paper discusses reliability-oriented challenges such as compression artifacts, interpretability issues, and the “liar’s dividend,” which undermines public trust in authentic media. The survey concludes by identifying gaps in current detection strategies and underscoring the urgent need for robust, scalable, and explainable solutions to mitigate the threats posed by deepfake technology while enabling its ethical use. Keywords - Deepfakes, GANs, Deepfake Detection, Synthetic Media, FaceForensics++.