Deepfake Video Detection: A Comprehensive Survey of Advanced Machine Learning and Deep Learning Techniques to Combat Synthetic Video Manipulation

The detection of deepfake videos has become a critical area of research due to the widespread use of manipulated content across digital platforms. With the increasing sophistication of generative models deepfake videos have reached a level of realism that makes them challenging to detect. This survey paper provides an extensive review of 70 research papers published from 2021 to 2024, categorizing the existing detection techniques into three primary groups such as supervised learning techniques, unsupervised learning techniques, and hybrid methods. These methods leverage labelled datasets to train models to identify subtle artefacts in manipulated videos. Notable advancements have been made by integrating CNNs with RNNs to capture both spatial and temporal patterns, enhancing detection accuracy. Unsupervised learning techniques, such as clustering algorithms and anomaly detection methods, are increasingly being explored for deepfake detection. Hybrid methods combine the strengths of both supervised and unsupervised techniques, improving detection performance. CNN-RNN architectures integrated with attention mechanisms are an example, as they enhance the focus on manipulated regions within videos. Additionally, multimodal approaches that combine both visual and audio cues have been introduced to address the complexity of detecting deepfakes across various media types. This review identifies the challenges faced by current detection systems, such as limitations in datasets, high computational costs, and a lack of robustness against evolving manipulation techniques. To address these challenges, future research should focus on developing more diverse datasets, enhancing the generalizability of models, and improving computational efficiency to support real-time applications. The most promising approach for advancing deepfake detection systems involves integrating multi-modal techniques, advanced deep learning architectures, and scalable designs.