中圖分類號： TP181
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.02.006
引用格式： 楊雨鑫，周欣，熊淑華，等. 融合傳統(tǒng)特征與神經(jīng)網(wǎng)絡(luò)的深度偽造檢測算法[J].信息技術(shù)與網(wǎng)絡(luò)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)