Video Fake Detection Using Of Convolutional and Gated Recurrent Neural Networks

Deep fake technology provides the possibility of automatic production and creation of (fake) video content through adversarial networks. Deep fake technology is a very challenging technology with very wide dimensions in various issues that have a deep impact on society, for example, this technology may cause bias in elections, create immoral content for the purpose of extortion, create political trends, etc. Much research has been devoted to the development of detection methods to reduce the potential negative impact of deep fake. The use of neural networks and deep learning is a common approach among all the research done in this field. In this research, the combination of GRU and LSTM neural networks has been used to detect fake. First, the Resnet neural network was used to extract the features of each frame, and then the GRU and LSTM layers were trained using these features, and finally, the fully connected layer was trained for the purpose of classification. The classification of input samples is used. To train and evaluate the proposed method, the training and evaluation datasets of FaceForensics++, Celeb-DF and Deepfake Detection Challenge have been used. The results show that the proposed method has obtained 97, 93 and 84% accuracy of diagnosis for the mentioned data sets and these results have improved by more than 2% compared to the reference research.