Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks
Face antispoofing detection aims to identify whether the user’s face identity information is legal. Multimodality models generally have high accuracy. However, the existing works of face antispoofing detection have the problem of insufficient research on the safety of the model itself. Therefore, th...
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Series: | Security and Communication Networks |
Online Access: | http://dx.doi.org/10.1155/2021/3670339 |
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doaj-15cedadaee6448c0b5cb6179297b4ac72021-08-23T01:33:16ZengHindawi-WileySecurity and Communication Networks1939-01222021-01-01202110.1155/2021/3670339Research on Multimodality Face Antispoofing Model Based on Adversarial AttacksJunjie Mao0Bin Weng1Tianqiang Huang2Feng Ye3Liqing Huang4College of Mathematics and InformaticsCollege of Mathematics and InformaticsCollege of Mathematics and InformaticsCollege of Mathematics and InformaticsCollege of Mathematics and InformaticsFace antispoofing detection aims to identify whether the user’s face identity information is legal. Multimodality models generally have high accuracy. However, the existing works of face antispoofing detection have the problem of insufficient research on the safety of the model itself. Therefore, the purpose of this paper is to explore the vulnerability of existing face antispoofing models, especially multimodality models, when resisting various types of attacks. In this paper, we firstly study the resistance ability of multimodality models when they encounter white-box attacks and black-box attacks from the perspective of adversarial examples. Then, we propose a new method that combines mixed adversarial training and differentiable high-frequency suppression modules to effectively improve model safety. Experimental results show that the accuracy of the multimodality face antispoofing model is reduced from over 90% to about 10% when it is attacked by adversarial examples. But, after applying the proposed defence method, the model can still maintain more than 90% accuracy on original examples, and the accuracy of the model can reach more than 80% on attack examples.http://dx.doi.org/10.1155/2021/3670339 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junjie Mao Bin Weng Tianqiang Huang Feng Ye Liqing Huang |
spellingShingle |
Junjie Mao Bin Weng Tianqiang Huang Feng Ye Liqing Huang Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks Security and Communication Networks |
author_facet |
Junjie Mao Bin Weng Tianqiang Huang Feng Ye Liqing Huang |
author_sort |
Junjie Mao |
title |
Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks |
title_short |
Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks |
title_full |
Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks |
title_fullStr |
Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks |
title_full_unstemmed |
Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks |
title_sort |
research on multimodality face antispoofing model based on adversarial attacks |
publisher |
Hindawi-Wiley |
series |
Security and Communication Networks |
issn |
1939-0122 |
publishDate |
2021-01-01 |
description |
Face antispoofing detection aims to identify whether the user’s face identity information is legal. Multimodality models generally have high accuracy. However, the existing works of face antispoofing detection have the problem of insufficient research on the safety of the model itself. Therefore, the purpose of this paper is to explore the vulnerability of existing face antispoofing models, especially multimodality models, when resisting various types of attacks. In this paper, we firstly study the resistance ability of multimodality models when they encounter white-box attacks and black-box attacks from the perspective of adversarial examples. Then, we propose a new method that combines mixed adversarial training and differentiable high-frequency suppression modules to effectively improve model safety. Experimental results show that the accuracy of the multimodality face antispoofing model is reduced from over 90% to about 10% when it is attacked by adversarial examples. But, after applying the proposed defence method, the model can still maintain more than 90% accuracy on original examples, and the accuracy of the model can reach more than 80% on attack examples. |
url |
http://dx.doi.org/10.1155/2021/3670339 |
work_keys_str_mv |
AT junjiemao researchonmultimodalityfaceantispoofingmodelbasedonadversarialattacks AT binweng researchonmultimodalityfaceantispoofingmodelbasedonadversarialattacks AT tianqianghuang researchonmultimodalityfaceantispoofingmodelbasedonadversarialattacks AT fengye researchonmultimodalityfaceantispoofingmodelbasedonadversarialattacks AT liqinghuang researchonmultimodalityfaceantispoofingmodelbasedonadversarialattacks |
_version_ |
1721198836911702016 |