Federated Face Forgery Detection Learning with Personalized Representation

Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralized training on data and lacked consideration of information sharing in non-public video data scenarios and data privacy. Naturally, the federated learning strategy can be applied for privacy protection, which aggregates model parameters of clients but not original data. However, simple federated learning can't achieve satisfactory performance because of poor generalization capabilities for the real hybrid-domain forgery dataset. To solve the problem, the paper proposes a novel federated face forgery detection learning with personalized representation. The designed Personalized Forgery Representation Learning aims to learn the personalized representation of each client to improve the detection performance of individual client models. In addition, a personalized federated learning training strategy is utilized to update the parameters of the distributed detection model. Here collaborative training is conducted on multiple distributed client devices, and shared representations of these client models are uploaded to the server side for aggregation. Experiments on several public face forgery detection datasets demonstrate the superior performance of the proposed algorithm compared with state-of-the-art methods. The code is available at \emph{https://github.com/GANG370/PFR-Forgery.}

FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

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.