Published Paper Details:

A powerful tool for deep fake detection has been developed with the advent of Long Short-Term Memory (LSTM) networks, a kind of Recurrent Neural Network (RNN) designed to process sequential data. LSTMs, in contrast to regular neural networks, retain information over long periods, making them ideal for investigating links and patterns in picture data that occur over time. Inconsistencies or anomalies that don't appear in individual frames but manifest themselves in analysis over time might be spotted using this expertise. An important step forward in the battle against online disinformation has been the development of a deepfake face recognition system that employs LSTM and machine learning. An advanced method for identifying deepfake material is provided by the system via the use of spatial feature extraction in conjunction with long short-term memory networks for temporal analysis. It can detect artefacts and small discrepancies that humans would miss since it can examine pictures from a spatial and temporal perspective. From social media to forensic investigation, the system's modular architecture guarantees versatility and real-time processing capabilities.