Emerging Innovations in Deep Learning for Video DeepFake Detection: A Comprehensive Review
Ms. Kaushani Makwana, Dheeraj Kumar Singh, Shaleen Shukla · 2025
The fast growth of artificial intelligence technology allowed researchers to develop highly believable deepfake media that causes significant privacy breaches while being used for legitimate educational and entertainment purposes. Modern deep generative models are evolving at a fast pace so detecting manipulated media has emerged as an essential requirement. An analysis examines the latest advancements in deepfake detection through an assessment of machine learning (ML) and deep learning (DL) methods particularly outlined by transfer learning and transformer-based systems. The evaluation analyzes modern approaches that detect artificial auditory and visual abnormalities which also solve problems with restricted datasets and the challenges of cross-manipulation and adversarial resistance. This study merges current scholarly research to show how modern detection systems perform yet stresses that more trustworthy detection solutions with enhanced efficiency need to develop. The rise of digital media in society demands sustained investment for research and technology development in video security protection. The progress of advanced deepfakes calls for organization-wide innovations in detection frameworks using AI-based solutions that must be developed to minimize their threats to digital content protection.