Detecting deepfake videos using machine learning

This project aims to develop effective machine learning algorithms for detecting deepfake videos, which have become a significant concern for disinformation and cybersecurity. The objectives include pre-processing the data for feature extraction, and training machine learning models to accurately classify videos as either real or manipulated. The methodology involves using advanced techniques such as convolutional neural networks, recurrent neural networks or video vision transformer models to analyse visual and temporal patterns in the videos. In addition, techniques like facial recognition, frame analysis, and optical flow detection may also be employed to identify inconsistencies or artifacts typical of deepfake generation. These techniques may help to enhance the video deepfake detection performance.