An intelligent adaptive learning framework for fake video detection using spatiotemporal features

Nowadays, multimedia is vulnerable to hacking because of insecurity. The traditional security mechanism is insufficient to deal with multimedia to protect them against malicious events. So, the present study has introduced a novel grey wolf-based YOLO spatiotemporal framework (GW-YSTF) for predicting frames, whether it is fake or real from the trained video data. After initializing the data, the function pre-processing is activated in the hidden layer of the GW-YSTF to eliminate the noisy features in the introduced video frames. Then, a feature analysis function was performed to select the needed parts. Henceforth, the fake video frames are predicted based on the different classes in the trained deepfake video database. Moreover, the presented model is tested in the Python environment. The improvement measure was validated in comparative analysis by comparing the proposed model performance with other existing models based on accuracy, recall, F-score, and precision. The proposed model has recorded the most comprehensive fake score for the accuracy of video frame prediction of 99.8%, higher than the traditional approaches.