Spoofing and countermeasures for speaker verification: A survey

Wu, Zhizheng; Evans, Nicholas; Kinnunen, Tomi; Yamagishi, Junichi; Alegre, Federico; Li, Haizhou

doi:10.1016/j.specom.2014.10.005

Cited by 553 publications

(357 citation statements)

References 138 publications

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“…for fingerprint recognition [3], face recognition [4] and speaker recognition [5]. The intention is also to stimulate further work, particularly the development of standard metrics, protocols and datasets for evaluating progress.…”

Section: Introductionmentioning

confidence: 99%

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Hadid

Evans²,

Marcel

et al. 2015

IEEE Signal Process. Mag.

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188

103

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Biometrics already form a significant component of current and emerging identification technologies.Biometrics systems aim to determine or verify the identity of an individual from their behavioural and/or biological characteristics. Despite significant progress, some biometric systems fail to meet the multitude of stringent security and robustness requirements to support their deployment in some practical scenarios.Among current concerns are vulnerabilities to spoofing, namely persons who masquerade as others in order to gain illegitimate accesses to protected data, services or facilities. While the study of spoofing, or rather anti-spoofing, has attracted growing interest in recent years, the problem is far from being solved and will require far greater attention in coming years. This tutorial article presents an introduction to spoofing and anti-spoofing research. It describes the vulnerabilities, presents an evaluation methodology for the assessment of spoofing and countermeasures, and outlines research priorities for the future.

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Section: Introductionmentioning

confidence: 99%

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Hadid

Evans²,

Marcel

et al. 2015

IEEE Signal Process. Mag.

Self Cite

188

103

View full text Add to dashboard Cite

show abstract

“…A survey by Wu et al [7] provides a comprehensive overview of both the existing spoofing attacks and the available attack detection approaches. An overview of the methods for synthetic speech detection by Sahidullah et al [15] benchmarks several existing feature extraction methods and classifiers on ASVspoof database.…”

Section: Featuresmentioning

confidence: 99%

“…Therefore, developing mechanisms for detection of presentation attacks is gaining interest in the speech community [7]. In that regard, the emphasis until now has been on logical access attacks, largely thanks to the "Automatic Speaker Verification Spoofing and Countermeasures Challenge" [4], which provided a large benchmark corpus containing voice conversion-based and speech synthesis-based attacks.…”

Section: Introductionmentioning

confidence: 99%

Presentation attack detection in voice biometrics

Korshunov¹,

Marcel²

2017

User-Centric Privacy and Security in Biometrics

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Recent years have shown an increase in both the accuracy of biometric systems and their practical use. The application of biometrics is becoming widespread with fingerprint sensors in smartphones, automatic face recognition in social networks and video-based applications, and speaker recognition in phone banking and other phone-based services. The popularization of the biometric systems, however, exposed their major flaw -high vulnerability to spoofing attacks [1]. A fingerprint sensor can be easily tricked with a simple glue-made mold, a face recognition system can be accessed using a printed photo, and a speaker recognition system can be spoofed with a replay of pre-recorded voice. The ease with which a biometric system can be spoofed demonstrates the importance of developing efficient anti-spoofing systems that can detect both known (conceivable now) and unknown (possible in the future) spoofing attacks.Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing biometric systems for practical and attack-resistant solutions. To be practical, however, an attack detection should have (i) high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient.One reason for the increasing demand for effective presentation attack detection (PAD) systems is the ease of access to people's biometric data. So often, a potential attacker can almost effortlessly obtain necessary biometric samples from social networks, including facial images, audio and video recordings, and even extract fingerprints from high resolution images. Therefore, various privacy protection solutions, such as legal privacy requirements and algorithms for obfuscating personal information, e.g., visual privacy filters [2], as well as, social awareness of threats to privacy can also increase security of personal information and potentially reduce the vulnerability of biometric systems.In this chapter, however, we focus on presentation attacks detection in voice biometrics, i.e., automatic speaker verification (ASV) systems. We discuss vulnerabilities of these systems to presentation attacks (PAs), present different state of the art 1

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“…3;2015 prompted to pronounce random words offered instantly by the system. However, the free choice of spoken sentences is allowed in text-independent voice authentication systems (Bellegarda & Silverman, 2014;Z. Wu et al, 2015).…”

Section: Voice Featuresmentioning

confidence: 99%

Authentication systems: principles and threats

Abdulkader¹,

Atia²,

Mostafa³

2015

CIS

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Identity manipulation is considered a serious security issue that has been enlarged with the spread of automated systems that could be accessed either locally or remotely. Availability, integrity, and confidentiality represent the basic requirements that should be granted for successful authentication systems. Personality verification has taken multiple forms depending on different possession types. They are divided into knowledge based, token based, and biometric based authentication. The permanent ownership to the human being has increased the chances of deploying biometrics based authentication in highly secure systems. It includes capturing the biological traits, which are physiological or behavioral, extracting the important features and comparing them to the previously stored features that belong to the claimed user. Various kinds of attacks aim to take down the basic requirements at multiple points. This paper describes different types of authentication along with their vulnerable points and threatening attacks. Then it provides more details about the biometric system structure as well as examples of distinguishing biological characteristics, organized by their locations. It shows the performance results of various biometric systems along with the deployed algorithms for different components.

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Spoofing and countermeasures for speaker verification: A survey

Cited by 553 publications

References 138 publications

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Presentation attack detection in voice biometrics

Authentication systems: principles and threats

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