Channel pattern noise based playback attack detection algorithm for speaker recognition

Wang, Zhifeng; Wang, Gang; He, Qianhua

doi:10.1109/icmlc.2011.6016982

Cited by 99 publications

(41 citation statements)

References 5 publications

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“…Such algorithms were reported in [37], for which the EER of a baseline GMM-UBM system was shown to decrease from 40 % to 10 % with active countermeasures. Another replay countermeasure aimed at detecting far-field recordings, which are unlikely in natural access scenarios where the speaker is usually close to the microphone [36].…”

Section: Spoofing Countermeasures For Asv Systemsmentioning

confidence: 99%

Increasing anti-spoofing protection in speaker verification using linear prediction

Janicki

2016

Multimed Tools Appl

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This article addresses the problem of anti-spoofing protection in an automatic speaker verification (ASV) system. An improved version of a previously proposed spoofing countermeasure is presented. The presented method is based on the analysis of linear prediction error that results from both short-and long-term prediction of the input speech signal. It was observed that non-natural speech signals, i.e., synthetic or converted speech, were predicted in a different way than genuine speech. Therefore, in contrast to the classical linear prediction analysis, where usually only the prediction coefficients are analyzed, in the proposed approach the residual (error) signals were examined. During this analysis, 23 various prediction parameters were extracted, such as the energy of the prediction error, prediction gains and temporal parameters related to the prediction error signals. Various binary classifiers were researched to separate human and spoof classes, however the support vector machines with radial basis function (SVM-RBF) yielded the best results. When tested on the corpora provided for the ASVspoof 2015 Challenge, the proposed countermeasure returned better results than the previous version of the algorithm and, in most of the cases, the baseline spoofing detector based on the local binary patterns (LBP). It is hoped that the proposed method can be part of a generalized spoofing countermeasure helping to increase security of ASV systems.The calculations described in the article were made in the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) of the University of Warsaw (computational grant No. G46-2).

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Section: Spoofing Countermeasures For Asv Systemsmentioning

confidence: 99%

Increasing anti-spoofing protection in speaker verification using linear prediction

Janicki

2016

Multimed Tools Appl

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“…A physical access scenario was considered in (Wang et al, 2011). Although no baseline statistics were reported, a textindependent GMM-UBM system was shown to give an EER of 40.17 % when subjected to replay attacks.…”

Section: Spoofingmentioning

confidence: 99%

“…Recently, due to the mass-market adoption of ASV techniques Nuance, 2013) and the awareness and simplicity of replay attacks, both industry (Nuance, 2013) and academia (Shang and Stevenson, 2010;Villalba and Lleida, 2011a,b;Wang et al, 2011) have shown an interest in developing replay attack countermeasures.…”

Section: Countermeasuresmentioning

confidence: 99%

Spoofing and countermeasures for speaker verification: A survey

Evans

Kinnunen

et al. 2015

Speech Communication

552

349

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While biometric authentication has advanced significantly in recent years, evidence shows the technology can be susceptible to malicious spoofing attacks. The research community has responded with dedicated countermeasures which aim to detect and deflect such attacks. Even if the literature shows that they can be effective, the problem is far from being solved; biometric systems remain vulnerable to spoofing. Despite a growing momentum to develop spoofing countermeasures for automatic speaker verification, now that the technology has matured sufficiently to support mass deployment in an array of diverse applications, greater effort will be needed in the future to ensure adequate protection against spoofing. This article provides a survey of past work and identifies priority research directions for the future. We summarise previous studies involving impersonation, replay, speech synthesis and voice conversion spoofing attacks and more recent efforts to develop dedicated countermeasures. The survey shows that future research should address the lack of standard datasets and the over-fitting of existing countermeasures to specific, known spoofing attacks.

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“…In the literature, detection of replay attacks has largely focused on characteristics related to channel noise and reverberation [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification

Muckenhirn

Magimai-Doss

Marcel

2016

2016 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Abstract-In recent years, there has been a growing interest in developing countermeasures against non zero-effort attacks for speaker verification systems. Until now, the focus has been on logical access attacks, where the spoofed samples are injected into the system through a software-based process. This paper investigates a more realistic type of attack, referred to as physical access or presentation attacks, where the spoofed samples are presented as input to the microphone. To detect such attacks, we propose a binary classifier based approach that uses longterm spectral statistics as feature input. Experimental studies on the AVspoof database, which contains presentation attacks based on replay, speech synthesis and voice conversion, shows that the proposed approach can yield significantly low detection error rate with a linear classifier (half total error rate of 0.038%). Furthermore, an investigation on Interspeech 2015 ASVspoof challenge dataset shows that it is equally capable of detecting logical access attacks.

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Channel pattern noise based playback attack detection algorithm for speaker recognition

Cited by 99 publications

References 5 publications

Increasing anti-spoofing protection in speaker verification using linear prediction

Increasing anti-spoofing protection in speaker verification using linear prediction

Spoofing and countermeasures for speaker verification: A survey

Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification

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