Deepfake Video Detection Using LSTM and XRESNET
Varun P. Shrivathsa · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: As the rapid evolution of Artificial Intelligence continues, it becomes increasingly crucial to implement robust measures for monitoring and combating the proliferation of Deepfake videos. In this proposed method, frame-level features are extracted from videos using the XResNet convolutional neural network. These extracted features serve as the foundation for training the LSTM (Long Short-Term Memory) Recurrent Neural Network, enabling it to classify videos as either real or fake. Our dataset originates from Meta DFDC (Deepfake Detection Challenge) videos, selected for both the training and testing phases of our model. This model is able to predict a video with an accuracy of 83.3%.