  With the continuous development of deep learning in the field of image
generation models, a large number of vivid forged faces have been generated and
spread on the Internet. These high-authenticity artifacts could grow into a
threat to society security. Existing face forgery detection methods directly
utilize the obtained public shared or centralized data for training but ignore
the personal privacy and security issues when personal data couldn't be
centralizedly shared in real-world scenarios. Additionally, different
distributions caused by diverse artifact types would further bring adverse
influences on the forgery detection task. To solve the mentioned problems, the
paper proposes a novel generalized residual Federated learning for face Forgery
detection (FedForgery). The designed variational autoencoder aims to learn
robust discriminative residual feature maps to detect forgery faces (with
diverse or even unknown artifact types). Furthermore, the general federated
learning strategy is introduced to construct distributed detection model
trained collaboratively with multiple local decentralized devices, which could
further boost the representation generalization. Experiments conducted on
publicly available face forgery detection datasets prove the superior
performance of the proposed FedForgery. The designed novel generalized face
forgery detection protocols and source code would be publicly available.
