Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey

Abstract

The rapid advancement of Generative Artificial Intelligence (GAI) has led to the proliferation of deepfake media, posing significant threats to digital security, privacy, and information integrity. To overcome this challenge, substantial research efforts have been directed toward developing automated detection techniques using deep learning methodologies. This study presents a comprehensive survey of deep learning-based deepfake detection methods, emphasizing lightweight and hybrid architectures designed for real-time deployment. The survey systematically categorizes the landscape of deepfake generation techniques and evaluates state-of-the-art detection frameworks, including: classical CNNs, efficient backbone architectures (MobileNet, EfficientNet), and spatiotemporal models (CNN LSTM/GRU). Furthermore, this study examines model compression techniques— including pruning and quantization — essential for resource-constrained deployment, and provides a structured analysis of benchmark datasets, major detection architecture categories, and persistent research gaps. By critically examining the trade-off between detection accuracy and computational latency, this paper identifies key open challenges and concludes by highlighting a research gap for probabilistically robust, lightweight frameworks, offering a roadmap for future research toward reliable, real-time deepfake forensics in unconstrained environments.