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Spoofing Attack Detection by Anomaly Detection

Fatemifar, Soroush, Arashloo, Shervin Rahimzadeh, Awais, Muhammad and Kittler, Josef (2019) Spoofing Attack Detection by Anomaly Detection In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), 12-17 May 2019, Brighton, UK.

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Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks. In this paper we adopt the anomaly detection approach proposed in [1], where the detector is trained on genuine accesses only using one-class classifiers and investigate the merit of subject specific solutions. We show experimentally that subject specific models are superior to the commonly used client independent method. We also demonstrate that the proposed approach is more robust than multiclass formulations to unseen attacks.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Arashloo, Shervin Rahimzadeh
Awais, Muhammad
Date : 2019
DOI : 10.1109/ICASSP.2019.8682253
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Face anti-spoofing; Anomaly detection; Client-specific information; One-class classification; Convolutional neural networks; Anomaly detection; Face; Support vector machines; Lighting; Biological system modeling; Face recognition; Protocols
Related URLs :
Depositing User : Clive Harris
Date Deposited : 30 Jul 2019 14:57
Last Modified : 30 Jul 2019 14:57

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