Detect forgery video by performing transfer learning on Deep Neural Network

Nowadays, the authenticity of digital image and videos becomes hard while the forgery techniques are more advanced. Given the recent progress on Generative Neural Network (GNN) development that may generate realistic images and videos, it becomes more difficult to detect the authenticity of digital photographs. In this thesis, we expose a popular open-source video forgery library called “DeepFaceLab” by making use of deep learning. We retrain the existing state-of-the-art image classification neural networks to capture the features from manipulated video frames. After passing various sets of forgery video frames through a well-trained neural network, a bottleneck file is created for each image, it contains the features and artifacts in forgery video that could not be captured by the human eye. Our testing accuracy is over 99% when testing DeepFake videos. We also examined our method on FaceForensics dataset and achieved good detection results on both testing set and validation set. Experiments under different data sizes confirm the effectiveness and efficiency of the proposed method.