Deep Fake Face Detection Using Long Short-Term Memory with Deep Learning Approach

Abstract

Strong and effective detection techniques are desperately needed to lessen the possible effects of disinformation and manipulation as the frequency of deepfake videos keeps rising. The use of Long Short-Term Memory (LSTM) networks for deepfake video detection is examined in this abstract. Recurrent neural networks (RNNs), such as LSTM, are a viable option for analysing dynamic movies because of their ability to capture temporal dependencies in sequential data. The study explores the complexities of using LSTM architectures to identify deepfake films and highlights the need of comprehending the temporal patterns present in manipulated information. Preprocessing video data as part of the suggested methodology entails producing training datasets of the highest Caliber and using data augmentation methods to improve model generalization. To attain the best results in deepfake detection, the training procedure and LSTM network-specific optimization techniques are investigated. Evaluation criteria including recall, accuracy, precision, and F1 score are used to evaluate how well the model works to discern between modified and authentic content. The abstract also covers potential directions for future study to strengthen the resilience of LSTM-based detection systems, as well as difficulties and constraints specific to deepfake detection, such as minimizing false positives and negatives. The results of this study have significance for practical uses, especially when it comes to social media and video hosting services, where the incorporation of LSTM-based deepfake identification can enhance online safety and security.