DeepFake Videos Detection and Classification Using Resnext and LSTM Neural Network

Abstract

Deepfake videos, which are artificial intelligence-altered videos that have acquired extensive awareness recently. Deep learning algorithms are utilized to create these deepfake films, which are meant to disseminate false information about anyone, including politicians and celebrities. These movies have been purposefully made viral in order to propagate propaganda and false information, to frighten people, and to destabilize society. It is exceedingly challenging for a casual viewer to recognize a deep fake video with the naked eye. Finding these manipulated films has been extremely difficult and requires careful attention. In order to recognize a deepfake movie, this paper, a novel methodology that combines ResNext, a Convolutional Neural Network (CNN) algorithm, with Long Short-Term Memory (LSTM), a Recurrent Neural Network (RNN) has been developed. It is found that as the number of epochs increased, the model's accuracy increased and its training loss reduced.