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.

翻译：随着深度学习在图像生成模型领域的持续发展，大量逼真的伪造人脸图像被生成并在互联网上传播。这些高真实性的伪造品可能对社会安全构成威胁。现有人脸伪造检测方法直接利用可获得的公开共享或集中式数据进行训练，但在实际场景中当个人数据无法集中共享时忽略了隐私与安全问题。此外，不同伪造类型导致的分布差异会对伪造检测任务产生不良影响。为解决上述问题，本文提出了一种新颖的广义残差联邦学习人脸伪造检测方法（FedForgery）。所设计的变分自编码器旨在学习鲁棒的判别性残差特征图，以检测具有多样（甚至未知）伪造类型的人脸。进一步引入通用联邦学习策略，构建由多个本地分布式设备协作训练的分布式检测模型，从而提升表征泛化能力。在公开人脸伪造检测数据集上的实验证明了所提出FedForgery方法的优越性能。所设计的新型广义人脸伪造检测协议及源代码将予以公开。