Multi-feature Fusion Face Authentication Model Based on Deep Learning
Face forgery poses a major challenge to network security.In response to the problem of existing face forgery models with single features and low accuracy,a multi-feature fusion face forgery model based on deep learning is proposed.The model designs different feature extraction modules to obtain feature representations at different scales.It also learns how to effectively fuse this semantic information to accurately determine whether it is forged,thereby significantly improving the accuracy and robustness of the model.Finally,a large number of experiments are carried out on the open data set FaceForensics++.The experimental results show that the designed model achieves significant performance improvement compared to existing methods.
Face forgery detectionFeature fusionDeep learningFaceForensics++dataset
NETL
NSTL
万方数据
