Deep Learning Based Deepfake Video Detection System

Deepfake technology involves the tampering or creation of synthetic media, the technology can be highly effective in undermining trust in digital content because it exploits developments in machine learning. A new algorithm is proposed with a hybrid deepfake detection framework that integrates CNNs and RNNs with the functionalities of spatial and temporal inconsistency detection. The proposed method, using FaceForensics++ and the Deepfake Detection Challenge (DFDC), comes with a detection accuracy of 92.5%. The study addresses challenges including bias in the dataset and computational efficiency as well as model generalization. The study contributes to applications in security, content verification, and real-time monitoring on social platforms.