Deepfake Video Detection Model Based on Consistency of Spatial-Temporal Features

In order to improve the feature utilization rate of the image to be detected, a Deepfake video detection model based on consistency of spatial-temporal features was proposed, inspired by the observation that there are slight inconsistency and discontinuity in the facial expression changes of the characters in Deepfake videos. In the model, the convolutional neural network (CNN) was used to extract the spatial features from the video to be detected, and an optical flow method was used to perform temporal features between consecutive frames of the video. Then another CNN was used to extract the abstract and in-depth features from the optical flow map. After the temporal features and spatial features were transformed from original representation space to a new feature space by neural networks, a fully connected neural network was used to classify the combined spatial and temporal features space to achieve the detection target. The model proposed in the paper was trained on the Faceforensics++, an open source Deepfake dataset. The experimental results indicated that the detection accuracy of the proposed model reaches 98.1%, and the AUC value reaches 0.998 1. By comparing with the existing Deepfake detection models, the proposed model is superior to the existing models in terms of detection accuracy and AUC value, which verifies the effectiveness of the proposed model. © 2020, Editorial Department of Advanced Engineering Sciences. All right reserved.