A facial geometry based detection model for face manipulation using CNN-LSTM architecture

Abstract

This issue of DeepFake technique that may cause great threat to privacy, democracy, and national security has attracted the attention of deep learning researchers. DeepFake detection, therefore, has been a very hot issue in deep learning research. The face landmark feature maps are often used by many DeepFake approaches in generating fake faces. It also provides key information to help to detect manipulated face images. In this paper, we propose a detection approach for manipulated face images. To make full use of face landmark information, we propose a facial geometry prior module (FGPM) to extract facial geometry feature maps. Then the facial geometry feature maps are embedded into upsampling feature maps generated by the CNN-LSTM network. The proposed FGPM exploits facial maps and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating the CNN-LSTM network. Finally, a decoder is used to learn the mapping from low-resolution feature maps to pixel-wise to predict manipulation localization. Or a softmax classifier is used to predict real or fake face images. By experimenting on several popular datasets, the proposed detection model has demonstrated the capability of localizing manipulation at the pixel level and with a high prediction on real or fake face images.