Detection and Prediction of Deep Fakes Using Advanced Deep Learning Algorithms A Novel Approach to Enhance Media Authenticity

This study, present a novel method with a unique combination of powerful deep learning techniques, specifically Long Short-Term Memory Networks (LSTM) networks, to detect and predict deep fakes. Deep fakes are a serious threat to media legitimacy because deep fakes produce very believable fake information. To exploit LSTMs' perceptive skills at analyzing temporal sequences, we exploit minute discrepancies in video frames beyond what traditional models can: Training and validation accuracy graph indicates 93% accuracy of the proposed model after training with diversified true and deepfake video datasets. This helps to show how well LSTM can tell true from false information, and made a reliable way to boost media authenticity. The paper provides evidence of how the results from our method can be used to improve the identification of deepfakes in order to facilitate additional efforts to defend digital material against the growing threat of media manipulation.