Efficiency Evaluation of Popular Deepfake Methods Using Convolution Neural Network

Many deepfake techniques in the early years are spread to create successful deepfake videos (i.e., Face Swap, Deep Fake, etc.). These methods enable anyone to manipulate faces in videos, which can negatively impact society. One way to reduce this problem is the deepfake detection. It has become such a hot topic and the most crucial task in recent years. This paper proposes a deep learning model to detect and evaluate deepfake video methods using convolutional neural networks. The model is evaluated on the FaceForensics++ video dataset that contains four different deepfake ways (deepfake, face 2 face, face swap, and neuraltexture), and it achieved 0.96 accuracy on the deepfake method, 0.95 accuracy on face 2 face approach, 0.94 precision on face swap method and 0.76 accuracy on neuraltexture method.