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Detecting manipulated facial images and videos on social networks has been anurgent problem to be solved. The compression of videos on social media hasdestroyed some pixel details that could be used to detect forgeries. Hence, itis crucial to detect manipulated faces in videos of different quality. Wepropose a new multi-stream network architecture named GGViT, which utilizesglobal information to improve the generalization of the model. The embedding ofthe whole face extracted by ViT will guide each stream network. Through a largenumber of experiments, we have proved that our proposed model achievesstate-of-the-art classification accuracy on FF++ dataset, and has been greatlyimproved on scenarios of different compression rates. The accuracy of Raw/C23,Raw/C40 and C23/C40 was increased by 24.34%, 15.08% and 10.14% respectively.

GGViT:Multistream Vision Transformer Network in Face2Face Facial Reenactment Detection