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With the rapid development of deep learning technology, more and more faceforgeries by deepfake are widely spread on social media, causing serious socialconcern. Face forgery detection has become a research hotspot in recent years,and many related methods have been proposed until now. For those images withlow quality and/or diverse sources, however, the detection performances ofexisting methods are still far from satisfactory. In this paper, we propose animproved Xception with dual attention mechanism and feature fusion for faceforgery detection. Different from the middle flow in original Xception model,we try to catch different high-semantic features of the face images usingdifferent levels of convolution, and introduce the convolutional blockattention module and feature fusion to refine and reorganize thosehigh-semantic features. In the exit flow, we employ the self-attentionmechanism and depthwise separable convolution to learn the global informationand local information of the fused features separately to improve theclassification the ability of the proposed model. Experimental resultsevaluated on three Deepfake datasets demonstrate that the proposed methodoutperforms Xception as well as other related methods both in effectiveness andgeneralization ability.

Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection