基于深度学习的多特征融合人脸鉴伪模型

Multi-feature Fusion Face Authentication Model Based on Deep Learning

人脸伪造给网络安全带来了重大挑战。针对现有人脸鉴伪模型特征单一、准确率低的问题,提出了一种基于深度学习的多特征融合人脸鉴伪模型。该模型设计了不同特征提取模块,用以获取不同尺度的特征表示。并学习如何有效融合这些语义信息以准确判定是否伪造,从而显著提升模型的准确率和鲁棒性。最后在公开数据集FaceForensics++上进行大量实验验证。实验结果显示,与现有方法相比,设计的模型有明显的性能提升。

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.