Deepfake Video Detection by Combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
Yunes Al-Dhabi, Shuang Zhang
Abstract
Nowadays, people are facing an emerging problem called deepfake videos. These videos were created using deep learning technology. Some are created just for fun, while others are trying to manipulate your opinions, cause threats to your privacy, reputation, and so on. Sometimes, deepfake videos created using the latest algorithms can be hard to distinguish with the naked eye. That's why we need better algorithms to detect deepfake. The system we are going to present is based on a combination of CNN and RNN, as research shows that using CNN and RNN combined achieve better results. We are going to use a pre-trained CNN model called Resnext50. Using this, we save the time of training the model from scratch. The proposed system uses Resnext pretrained model for Feature Extraction and these extracted features are used to train the Long short-term memory (LSTM). Using CNN and RNN combined, we capture the inter frames as well as intra frames features which will be used to detect if the video is real or fake. We evaluated our method using a large collection of deepfake videos gathered from a variety of distribution sources. We demonstrate how our system may obtain competitive results while utilizing a simplistic architecture.