  With the rapid development of deep learning technology, more and more face
forgeries by deepfake are widely spread on social media, causing serious social
concern. Face forgery detection has become a research hotspot in recent years,
and many related methods have been proposed until now. For those images with
low quality and/or diverse sources, however, the detection performances of
existing methods are still far from satisfactory. In this paper, we propose an
improved Xception with dual attention mechanism and feature fusion for face
forgery detection. Different from the middle flow in original Xception model,
we try to catch different high-semantic features of the face images using
different levels of convolution, and introduce the convolutional block
attention module and feature fusion to refine and reorganize those
high-semantic features. In the exit flow, we employ the self-attention
mechanism and depthwise separable convolution to learn the global information
and local information of the fused features separately to improve the
classification the ability of the proposed model. Experimental results
evaluated on three Deepfake datasets demonstrate that the proposed method
outperforms Xception as well as other related methods both in effectiveness and
generalization ability.

Computer Vision and Pattern Recognition