A Novel Face Forgery Detection Method Based on Augmented Dual-Stream Networks

Abstract

The current face forgery methods based on deep learning are becoming more mature and abundant, and existing detection techniques have some limitations and applicability issues that make it difficult to effectively detect such behaviour. In this paper, we propose an enhanced dual-stream FC_2_stream network model based on dual-stream networks to detect forged regions in manipulated face images through end-to-end training of the images. The RGB stream is used to extract features from the RGB image to find the forged traces; the noise stream uses the filtering layer of the SRM (Steganalysis Rich Model) model to extract the noise features and find the inconsistency between the noise in the real region and the forged region in the fake face, then the features of the two streams are fused with a bilinear pooling layer to predict the forged region, and finally the forged region is determined by whether the blending boundary of the forged image is displayed to determine the image authenticity. Experiments conducted on four benchmark datasets show that our model is still effective against forgeries generated by unknown face manipulation methods, and also demonstrate the superior generalisation capability of our model.