An Overview of DeepFake Video Detection and Mitigation

The rapid development of deepfake technology, driven by artificial intelligence and machine learning advancements, creates a significant challenge to authenticity in digital media. With deep fakes, for instance, the manipulation of images, videos, or audio to create hyper-realistic but fabricated content forms a tool for spreading disinformation, committing cyber crimes, and further eroding public trust. This study systematically reviews the numerous methods implemented in deep fake detection research, such as deep learning-based techniques, machine learning approaches, statistical analysis, and blockchain technologies. To this end, it further classifies available detection methodologies, critically examines their respective effectiveness, and compares advantages and disadvantages for each category. Additionally, the importance of datasets to the process of training a model, a discussion on metrics, model generalization issues, as well as other related problems will be identified in the performance of the deepfake model. In addition, this study will aim to develop more efficient and scalable systems for deepfake detection so that the threat posed by media manipulation in the age of digital can be alleviated.