Advancements and Challenges in Deepfake Video Detection: A Comprehensive Review

Deepfake is a technique that takes advantage of deep learning techniques to make incredibly convincing phony videos or photos, frequently including folks stating or performing things they are unlikely to do. Its detection is the method of detecting if digital media material has already been edited or synthesized, making use of deep learning techniques. Deepfake detection is critical for ensuring the authenticity and credibility of digital media material in an era where AI-generated manipulations represent serious challenges to trust and security approaches. Impersonating persons, distributing false information, making fraudulent movies for blackmail, and altering digital content for harmful ends are all examples of deepfake scams that use synthetic media created by deep learning algorithms. Several research have been undertaken in recent times to better understand workings of deepfakes, and numerous deep learning approaches have emerged to detect deepfake videos or images. We investigate numerous methodologies, such as neural networks, spatiotemporal analysis, and multi-modal techniques, highlighting their advantages and disadvantages. This study gives a thorough examination of cutting-edge deepfake video detection algorithms. Finally, in an era of more complex manipulations, we propose research gaps and future approaches to enhance the resilience and efficacy of detection of deepfakes.