Federated Generalized Face Presentation Attack Detection

04/14/2021
by   Rui Shao, et al.
0

Face presentation attack detection plays a critical role in the modern face recognition pipeline. A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (both real face images and spoof images) are not directly shared between data owners due to legal and privacy issues. In this paper, with the motivation of circumventing this challenge, we propose a Federated Face Presentation Attack Detection (FedPAD) framework that simultaneously takes advantage of rich fPAD information available at different data owners while preserving data privacy. In the proposed framework, each data center locally trains its own fPAD model. A server learns a global fPAD model by iteratively aggregating model updates from all data centers without accessing private data in each of them. To equip the aggregated fPAD model in the server with better generalization ability to unseen attacks from users, following the basic idea of FedPAD, we further propose a Federated Generalized Face Presentation Attack Detection (FedGPAD) framework. A federated domain disentanglement strategy is introduced in FedGPAD, which treats each data center as one domain and decomposes the fPAD model into domain-invariant and domain-specific parts in each data center. Two parts disentangle the domain-invariant and domain-specific features from images in each local data center, respectively. A server learns a global fPAD model by only aggregating domain-invariant parts of the fPAD models from data centers and thus a more generalized fPAD model can be aggregated in server. We introduce the experimental setting to evaluate the proposed FedPAD and FedGPAD frameworks and carry out extensive experiments to provide various insights about federated learning for fPAD.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 6

page 8

05/29/2020

Federated Face Anti-spoofing

Face presentation attack detection plays a critical role in the modern f...
10/25/2021

Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation

Face presentation attack detection (fPAD) plays a critical role in the m...
05/06/2021

Federated Face Recognition

Face recognition has been extensively studied in computer vision and art...
03/22/2021

Improved Detection of Face Presentation Attacks Using Image Decomposition

Presentation attack detection (PAD) is a critical component in secure fa...
06/30/2020

Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector

In a typical face recognition pipeline, the task of the face detector is...
10/18/2021

Asymmetric Modality Translation For Face Presentation Attack Detection

Face presentation attack detection (PAD) is an essential measure to prot...
10/25/2021

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Federated learning has quickly gained popularity with its promises of in...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.