中图分类号： TP181
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2021.02.006
引用格式： 杨雨鑫，周欣，熊淑华，等. 融合传统特征与神经网络的深度伪造检测算法[J].信息技术与网络安全，2021，40(2)：33-38，44.

Research on deepfakes detection combining traditional features and neural network

Abstract： DeepFakes detection is significant to combat the spread of forgery video. Aiming at the task of deepfakes detection, a method combining traditional features and neural network is proposed. The method combines the interpretability of traditional features and high accuracy of the neural network. This paper used the gray level co-occurrence matrix and XceptionNet to form two feature extraction modules, then learned the dual-stream fusion feature information in the fully convolutional network. The image was distinguished according to multiple losses in the network finally. Our method was tested over benchmarks of the FaceForensics++ datasets. The experimental results show that compared with the state-of-the-art deep learning algorithms, the detection accuracy has been significantly improved. It shows promising discrimination performance due to the introduction of texture feature interpretability.

Key words : deepfakes；image forensics；feature fusion；gray level co-occurrence matrix(GLCM)；convolutional neural network(CNN)