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{this https URL}

提出了一种新颖的个性化伪造脸部检测学习方法，旨在通过个性化表示学习每个客户的个性化表示，提高个体客户模型的检测性能；同时，利用个性化联合学习训练策略更新分布式检测模型的参数，通过多个分布式客户端设备进行协同训练，并将这些客户端模型的共享表示上传到服务器端进行聚合。与现有方法相比，该算法在多个公共脸部伪造检测数据集上展现出卓越的性能。

个性化表示的联邦人脸伪造检测学习

Federated Face Forgery Detection Learning with Personalized  Representation

Detecting diffusion-generated images has recently grown into an emerging
research area. Existing diffusion-based datasets predominantly focus on general
image generation. However, facial forgeries, which pose a more severe social
risk, have remained less explored thus far. To address this gap, this paper
introduces DiFF, a comprehensive dataset dedicated to face-focused
diffusion-generated images. DiFF comprises over 500,000 images that are
synthesized using thirteen distinct generation methods under four conditions.
In particular, this dataset leverages 30,000 carefully collected textual and
visual prompts, ensuring the synthesis of images with both high fidelity and
semantic consistency. We conduct extensive experiments on the DiFF dataset via
a human test and several representative forgery detection methods. The results
demonstrate that the binary detection accuracy of both human observers and
automated detectors often falls below 30%, shedding light on the challenges in
detecting diffusion-generated facial forgeries. Furthermore, we propose an edge
graph regularization approach to effectively enhance the generalization
capability of existing detectors.

通过引入 DiFF 数据集，本文首次探索了面向脸部的扩散生成图像的领域，分析结果揭示了识别扩散生成的假脸面临的挑战，并提出了一种边缘图正则化方法来增强现有检测器的泛化能力。