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17
Spoofing countermeasures for the protection of automatic speaker recognition from attacks with artificial signals
- in Proc. 13th Interspeech
, 2012
"... Certain short intervals in a speech signal X, e.g. those corresponding to voiced regions, give rise to higher scores or likelihoods than others and the chances of a spoofing attack succeeding can thus be increased by concentrating on a short interval or sequence of frames in X = {x1,..., xm} which g ..."
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Certain short intervals in a speech signal X, e.g. those corresponding to voiced regions, give rise to higher scores or likelihoods than others and the chances of a spoofing attack succeeding can thus be increased by concentrating on a short interval or sequence of frames in X = {x1,..., xm} which gives rise to the highest score. Let T = {t1,..., tn} be such an interval short enough so that all frames in the interval provoke high scores, but long enough so that relevant dynamic information (e.g. delta and acceleration coefficients) can be captured and/or modeled. In order to produce a sample of significant duration, T can be replicated and concatenated any number of times to produce an audio signal of arbitrary length. In practice, the resulting concatehal-00783789,
Spoofing and countermeasures for automatic speaker verification
"... It is widely acknowledged that most biometric systems are vulnerable to spoofing, also known as imposture. While vulnerabilities and countermeasures for other biometric modalities have been widely studied, e.g. face verification, speaker verification systems remain vulnerable. This paper describes s ..."
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It is widely acknowledged that most biometric systems are vulnerable to spoofing, also known as imposture. While vulnerabilities and countermeasures for other biometric modalities have been widely studied, e.g. face verification, speaker verification systems remain vulnerable. This paper describes some specific vulnerabilities studied in the literature and presents a brief survey of recent work to develop spoofing countermeasures. The paper concludes with a discussion on the need for standard datasets, metrics and formal evaluations which are needed to assess vulnerabilities to spoofing in realistic scenarios without prior knowledge. Index Terms: spoofing, imposture, automatic speaker verification 1.
A new speaker verification spoofing countermeasure based
, 2013
"... on local binary patterns ..."
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Evasion and obfuscation in automatic speaker verification, in
- Proc. IEEE Int
, 2014
"... The potential for biometric systems to be manipulated through some form of subversion is well acknowledged. One such ap-proach known as spoofing relates to the provocation of false accepts in authentication applications. Another approach re-ferred to as obfuscation relates to the provocation of miss ..."
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The potential for biometric systems to be manipulated through some form of subversion is well acknowledged. One such ap-proach known as spoofing relates to the provocation of false accepts in authentication applications. Another approach re-ferred to as obfuscation relates to the provocation of missed detections in surveillance applications. While the automatic speaker verification research community is now addressing spoofing and countermeasures, vulnerabilities to obfuscation remain largely unknown. This paper reports the first study. Our work with standard NIST datasets and protocols shows that the equal error rate of a standard GMM-UBM system is increased from 9 % to 48 % through obfuscation, whereas that of a state-of-the-art i-vector system increases from 3 % to 20%. We also present a generalised approach to obfuscation detection which succeeds in detecting almost all attempts to evade detection. Index Terms — evasion, obfuscation, speaker recogni-tion, speaker verification, surveillance, biometrics, spoofing
Joint speaker verification and anti-spoofing in the i-vector space
- IEEE Trans. on Information Forensics and Security
, 2015
"... Abstract—Any biometric recognizer is vulnerable to spoofing attacks and hence voice biometric, also called automatic speaker verification (ASV), is no exception; replay, synthesis and conver-sion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) ..."
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Abstract—Any biometric recognizer is vulnerable to spoofing attacks and hence voice biometric, also called automatic speaker verification (ASV), is no exception; replay, synthesis and conver-sion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks considered as one of the most challenging for modern recognition systems. To detect spoofing, most existing countermeasures assume explicit or implicit knowledge of a particular VC system and focus on designing discriminative features. In this work, we explore back-end generative models for more generalized countermeasures. Specifically, we model synthesis-channel subspace to perform speaker verification and anti-spoofing jointly in the i-vector space, which is a well-established technique for speaker modeling. It enables us to integrate speaker verification and anti-spoofing tasks into one system without any fusion techniques. To validate the proposed approach, we study vocoder-matched and vocoder-mismatched ASV and VC spoofing detection on the NIST 2006 speaker recognition evaluation dataset. Promising results are obtained for standalone countermeasures as well as their combination with ASV systems using score fusion and joint approach. Index Terms—speaker recognition, spoofing, voice conversion attack, i-vector, joint verification and anti-spoofing.
Classifiers for Synthetic Speech Detection: A Comparison
"... Automatic speaker verification (ASV) systems are highly vul-nerable against spoofing attacks, also known as imposture. With recent developments in speech synthesis and voice conversion technology, it has become important to detect synthesized or voice-converted speech for the security of ASV systems ..."
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Automatic speaker verification (ASV) systems are highly vul-nerable against spoofing attacks, also known as imposture. With recent developments in speech synthesis and voice conversion technology, it has become important to detect synthesized or voice-converted speech for the security of ASV systems. In this paper, we compare five different classifiers used in speaker recognition to detect synthetic speech. Experimental results conducted on the ASVspoof 2015 dataset show that support vector machines with generalized linear discriminant kernel (GLDS-SVM) yield the best performance on the development set with the EER of 0.12 % whereas Gaussian mixture model (GMM) trained using maximum likelihood (ML) criterion with the EER of 3.01 % is superior for the evaluation set. Index Terms: spoof detection, countermeasures, speaker recognition
AuthLoop: End-to-End Cryptographic Authentication for Telephony over Voice Channels AuthLoop: Practical End-to-End Cryptographic Authentication for Telephony over Voice Channels
, 2016
"... Abstract Telephones remain a trusted platform for conducting some of our most sensitive exchanges. From banking to taxes, wide swathes of industry and government rely on telephony as a secure fall-back when attempting to confirm the veracity of a transaction. In spite of this, authentication is poo ..."
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Abstract Telephones remain a trusted platform for conducting some of our most sensitive exchanges. From banking to taxes, wide swathes of industry and government rely on telephony as a secure fall-back when attempting to confirm the veracity of a transaction. In spite of this, authentication is poorly managed between these systems, and in the general case it is impossible to be certain of the identity (i.e., Caller ID) of the entity at the other end of a call. We address this problem with AuthLoop, the first system to provide cryptographic authentication solely within the voice channel. We design, implement and characterize the performance of an in-band modem for executing a TLS-inspired authentication protocol, and demonstrate its abilities to ensure that the explicit single-sided authentication procedures pervading the web are also possible on all phones. We show experimentally that this protocol can be executed with minimal computational overhead and only a few seconds of user time (≈ 9 instead of ≈ 97 seconds for a naïve implementation of TLS 1.2) over heterogeneous networks. In so doing, we demonstrate that strong end-to-end validation of Caller ID is indeed practical for all telephony networks.
Security Evaluation of i-Vector Based Speaker Verification Systems Against Hill-Climbing Attacks
"... This work studies the vulnerabilities of i-vector based speaker verification systems against indirect attacks. Particularly, we exploit the one-to-one representation of speakers via their corresponding i-vectors to perform Hill-Climbing based attacks; under the hypothesis that the inherent low-dimen ..."
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This work studies the vulnerabilities of i-vector based speaker verification systems against indirect attacks. Particularly, we exploit the one-to-one representation of speakers via their corresponding i-vectors to perform Hill-Climbing based attacks; under the hypothesis that the inherent low-dimensionality of i-vectors might represent a potential security breach to fraudulently access the system. The conducted attacks followed a standard experimental protocol already applied to other biometric systems based on face or signature; and they were tested against a state-of-art PLDA speaker verification system in the framework of the NIST SRE 2010 evaluation campaign. Specifically, up to 200 speakers, 100 female and 100 male, were attacked supplanting their corresponding i-vectors by those derived with the Hill-Climbing approach. Experiments show the success of the proposed attack compared with those based on brute force, achieving high Success Rates (up to 100%) and needing half as many comparisons as the brute force access attempts. These results evidence the vulnerability of i-vector based speaker verification systems in those scenarios where access to the matcher score is granted multiple times. As a countermeasure to minimize the effect of the attack score quantization is also evaluated. Index Terms: speaker verification, i-vector, security, hillclimbing. 1.
Author manuscript, published in "International Conference on Biometrics: Theory, Applications and Systems (BTAS) (2013) 8p" A One-Class Classification Approach to Generalised Speaker Verification Spoofing Countermeasures using Local Binary Patterns
, 2013
"... The vulnerability of automatic speaker verification systems to spoofing is now well accepted. While recent work has shown the potential to develop countermeasures capable of detecting spoofed speech signals, existing solutions typically function well only for specific attacks on which they are optim ..."
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The vulnerability of automatic speaker verification systems to spoofing is now well accepted. While recent work has shown the potential to develop countermeasures capable of detecting spoofed speech signals, existing solutions typically function well only for specific attacks on which they are optimised. Since the exact nature of spoofing attacks can never be known in practice, there is thus a need for generalised countermeasures which can detect previously unseen spoofing attacks. This paper presents a novel countermeasure based on the analysis of speech signals using local binary patterns followed by a one-class classification approach. The new countermeasure captures differences in the spectro-temporal texture of genuine and spoofed speech, but relies only on a model of the former. We report experiments with three different approaches to spoofing and with a state-of-the-art i-vector speaker verification system which uses probabilistic linear discriminant analysis for intersession compensation. While a support vector machine classifier is tuned with examples of converted voice, it delivers reliable detection of spoofing attacks using synthesized speech and artificial signals, attacks for which it is not optimised. 1.
