Preventing digital media tampering requires efficient identification of video forgeries. In order to provide exceptional results, Deep Convolutional Neural Networks (DCNNs) examine intricate patterns in video frames. In order to determine how well algorithms based on DCNN can identify false information, this study examines their performance in this area. It makes use of methods for extracting features, analyzing spatiotemporal data, and detecting anomalies. Improved capacity for deepfake and synthetic media recognition in real time is the goal of the suggested method. When compared to conventional methods, it finds more subtle anomalies in altered videos. The model is trained on a diverse array of datasets to enhance its adaptability to changing production procedures. The outcomes of the experiments suggest that it could be possible to identify fake content with more precision and consistency. They have practical uses in fields like journalism, cybersecurity, and forensic investigations. Improved digital authenticity and less misinformation are both made possible by this effort.
