Related papers: Federated Face Forgery Detection Learning with Personalized Representation

Federated Face Forgery Detection Learning with Personalized Representation

URL: http://arxiv.org/abs/2406.11145v1
Date: Mon, 17 Jun 2024 02:20:30 GMT
Title: Federated Face Forgery Detection Learning with Personalized Representation
Authors: Decheng Liu, Zhan Dang, Chunlei Peng, Nannan Wang, Ruimin Hu, Xinbo Gao,
Abstract summary: 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. The paper proposes a novel federated face forgery detection learning with personalized representation.
Score: 63.90408023506508
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.}

Related papers

FedHide: Federated Learning by Hiding in the Neighbors [12.71494268219787]
We propose a prototype-based federated learning method designed for embedding networks in classification or verification tasks. Our approach generates proxy class prototypes by linearly combining them with their nearest neighbors. This technique conceals the true class prototype while enabling clients to learn discriminative embedding networks.
arXiv Detail & Related papers (2024-09-12T07:37:49Z)
Personalized federated learning based on feature fusion [2.943623084019036]
Federated learning enables distributed clients to collaborate on training while storing their data locally to protect client privacy. We propose a personalized federated learning approach called pFedPM. In our process, we replace traditional gradient uploading with feature uploading, which helps reduce communication costs and allows for heterogeneous client models.
arXiv Detail & Related papers (2024-06-24T12:16:51Z)
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning [0.0]
Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. We propose a new representation learning-based approach that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods.
arXiv Detail & Related papers (2024-04-27T06:37:19Z)
Learn What You Need in Personalized Federated Learning [53.83081622573734]
$textitLearn2pFed$ is a novel algorithm-unrolling-based personalized federated learning framework. We show that $textitLearn2pFed$ significantly outperforms previous personalized federated learning methods.
arXiv Detail & Related papers (2024-01-16T12:45:15Z)
Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
arXiv Detail & Related papers (2023-09-14T03:48:27Z)
Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
arXiv Detail & Related papers (2023-03-07T14:11:01Z)
FedHP: Heterogeneous Federated Learning with Privacy-preserving [0.0]
Federated learning is a distributed machine learning environment, which ensures that clients complete collaborative training without sharing private data, only by exchanging parameters. We propose a novel federated learning method, which consists of the pre-trained model as the backbone and fully connected layers as the head. By sharing the embedding vector of classes, instead of parameters based on gradient space, clients can better adapt to private data, and it is more efficient in the communication between the server and clients.
arXiv Detail & Related papers (2023-01-27T13:32:17Z)
FedForgery: Generalized Face Forgery Detection with Residual Federated Learning [87.746829550726]
Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training. The paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery) Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery.
arXiv Detail & Related papers (2022-10-18T03:32:18Z)
Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
arXiv Detail & Related papers (2021-02-14T05:36:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.