Deepfake Detection Using Machine Learning: A Comprehensive Literature Review

Deepfake technology, driven by ML and DL, is both a creative tool and a threat to privacy, security, and trust. While it enables innovation in entertainment and education, its misuse raises ethical concerns. This paper explores deepfake detection, analyzing 33 studies (2020–2023). It compares ML-based methods, which are efficient but less accurate, with DL-based approaches that are precise but resource-intensive. Key factors include detection accuracy, real-world adaptability, and resistance to evasion tactics. Challenges include limited training data, scalability issues, and evolving deepfake techniques. This research identifies gaps and suggests improvements to ensure trust, accuracy, and usability in detection systems.