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Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing

Authors:

Jiashi FengAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 5

Article No.: 60, Pages 1 - 19

https://doi.org/10.1145/3402446

Published: 26 July 2020 Publication History

Abstract

Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of novel patterns or unseen scenes, leading to poor generalization performance. Furthermore, almost all current methods treat face anti-spoofing as a prior step to face recognition, which prolongs the response time and makes face authentication inefficient. In this article, we try to boost the generalizability and applicability of face anti-spoofing methods by designing a new generalizable face authentication CNN (GFA-CNN) model with three novelties. First, GFA-CNN introduces a simple yet effective total pairwise confusion loss for CNN training that properly balances contributions of all spoofing patterns for recognizing the spoofing faces. Second, it incorporate a fast domain adaptation component to alleviate negative effects brought by domain variation. Third, it deploys filter diversification learning to make the learned representations more adaptable to new scenes. In addition, the proposed GFA-CNN works in a multi-task manner—it performs face anti-spoofing and face recognition simultaneously. Experimental results on five popular face anti-spoofing and face recognition benchmarks show that GFA-CNN outperforms previous face anti-spoofing methods on cross-test protocols significantly and also well preserves the identity information of input face images.

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Cited By

Antil ADhiman C(2025)Unmasking Deception: A Comprehensive Survey on the Evolution of Face Anti‐spoofing MethodsNeurocomputing10.1016/j.neucom.2024.128992617(128992)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128992
Winarty S(2024)Representasi Identitas Virtual dalam Komunikasi Mahasiswa Urban di Instagram: Studi Netnografi pada Universitas Pembangunan JayaJurnal Bisnis dan Komunikasi Digital10.47134/jbkd.v1i3.25171:3(13)Online publication date: 20-May-2024
https://doi.org/10.47134/jbkd.v1i3.2517
Birla LSaikia TGupta P(2024)AVENUE: A Novel Deepfake Detection Method Based on Temporal Convolutional Network and rPPG InformationACM Transactions on Intelligent Systems and Technology10.1145/370223216:1(1-16)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3702232
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Index Terms

Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
        Image representations

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 5

Survey Paper and Regular Paper

October 2020

325 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3409643

Editor:
Yu Zheng
JD Digits, China

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Copyright © 2020 ACM.

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Publication History

Published: 26 July 2020

Accepted: 01 May 2020

Revised: 01 April 2020

Received: 01 November 2019

Published in TIST Volume 11, Issue 5

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Cited By

Antil ADhiman C(2025)Unmasking Deception: A Comprehensive Survey on the Evolution of Face Anti‐spoofing MethodsNeurocomputing10.1016/j.neucom.2024.128992617(128992)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128992
Winarty S(2024)Representasi Identitas Virtual dalam Komunikasi Mahasiswa Urban di Instagram: Studi Netnografi pada Universitas Pembangunan JayaJurnal Bisnis dan Komunikasi Digital10.47134/jbkd.v1i3.25171:3(13)Online publication date: 20-May-2024
https://doi.org/10.47134/jbkd.v1i3.2517
Birla LSaikia TGupta P(2024)AVENUE: A Novel Deepfake Detection Method Based on Temporal Convolutional Network and rPPG InformationACM Transactions on Intelligent Systems and Technology10.1145/370223216:1(1-16)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3702232
Pooshideh MBeheshti AQi YFarhood HSimpson MGatland NSoltany M(2024)Presentation Attack Detection: A Systematic Literature ReviewACM Computing Surveys10.1145/368726457:1(1-32)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3687264
Huang JZhou DLiu JShi LChen S(2024)IFAST: Weakly Supervised Interpretable Face Anti-Spoofing From Single-Shot Binocular NIR ImagesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.346593019(9270-9284)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3465930
Qian YChen KWang BGu ZJi SWang WZhang Y(2024)Enhancing Transferability of Adversarial Examples Through Mixed-Frequency InputsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.343050819(7633-7645)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3430508
Zhang YYe DXie CTang LLiao XLiu ZChen CDeng J(2024)Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking Against Face SwappingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338364819(4628-4641)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3383648
Jiang FLiu YSi HMeng JLi Q(2024)Cross-Scenario Unknown-Aware Face Anti-Spoofing With Evidential Semantic Consistency LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.335623419(3093-3108)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3356234
Sun XCheng GLi HPeng HHan J(2024)Task-Specific Importance-Awareness Matters: On Targeted Attacks Against Object DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.342565534:11_Part_2(11619-11629)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3425655
Qi HHan RShi YQi X(2024)A Novel High-Performance Face Anti-Spoofing Detection MethodIEEE Access10.1109/ACCESS.2024.340028512(67379-67391)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3400285
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