Speaker verification security improvement by means of speech watermarking

Faundez-Zanuy, Marcos; Hagmüller, Martín; Kubin, Gernot

doi:10.1016/j.specom.2006.06.010

Cited by 37 publications

(26 citation statements)

References 9 publications

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“…Since neither microphone level nor transmission level attacks necessarily require system-level access, they are the most easily implemented attacks and are thus the greatest threat to typical ASV systems (Faundez-Zanuy et al, 2006). They are accordingly the focus in the remainder of this paper.…”

Section: Possible Attack Pointsmentioning

confidence: 99%

“…In the specific case of ASV as illustrated in Figure 1, attacks at both the microphone and transmission levels are generally considered to pose the greatest threat (Faundez-Zanuy et al, 2006). Here, an adversary, typically referred to as an impostor, might seek to deceive the system by impersonating another enrolled user at the microphone in order to manipulate the ASV result.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spoofing and countermeasures for speaker verification: A survey

Evans

Kinnunen

et al. 2015

Speech Communication

540

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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Section: Possible Attack Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spoofing and countermeasures for speaker verification: A survey

Evans

Kinnunen

et al. 2015

Speech Communication

540

349

View full text Add to dashboard Cite

show abstract

“…As pointed out in [11,45], spoofing attacks can take place at two locations in a speaker verification system: at the microphone sensor and during the transmission of the acquired speech signal. At the sensor level, an impostor, also called an adversary, could compromise the system by replaying a pre-recorded speech signal or impersonating the target speaker at the sensor.…”

Section: Vulnerability Of Speaker Verification To Attacksmentioning

confidence: 99%

On the study of replay and voice conversion attacks to text-dependent speaker verification

2015

Multimed Tools Appl

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Automatic speaker verification (ASV) is to automatically accept or reject a claimed identity based on a speech sample. Recently, individual studies have confirmed the vulnerability of state-of-the-art text-independent ASV systems under replay, speech synthesis and voice conversion attacks on various databases. However, the behaviours of text-dependent ASV systems have not been systematically assessed in the face of various spoofing attacks. In this work, we first conduct a systematic analysis of text-dependent ASV systems to replay and voice conversion attacks using the same protocol and database, in particular the RSR2015 database which represents mobile device quality speech. We then analyse the interplay of voice conversion and speaker verification by linking the voice conversion objective evaluation measures with the speaker verification error rates to take a look at the vulnerabilities from the perspective of voice conversion.

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“…However, the systems can be protected and secured against these attacks by time stamps and watermarking. Recently, speech watermarking has been used to secure communication channels for speaker verification and identification against both intentional and unintentional attacks [3][4][5][6]. For this purpose, the watermark is embedded to verify both the authenticity of the transmitter (i.e., using sensor and feature extractors) and the integrity of the entire authentication mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…However, these techniques can seriously degrade speaker recognition performance. Also, watermarking and speaker recognition systems have opposing goals whenever the signal-to-watermark ratio (SWR) is reduced and the robustness of the watermark is increased, and the speaker identification and verification performance can be reduced [3][4][5]10]. Some researchers therefore apply semi-fragile watermarking methods to reduce the effects on recognition performance [11].…”

Section: Introductionmentioning

confidence: 99%

Semi-fragile digital speech watermarking for online speaker recognition

Nematollahi

Akhaee

Al-Haddad

et al. 2015

J AUDIO SPEECH MUSIC PROC.

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In this paper, a semi-fragile and blind digital speech watermarking technique for online speaker recognition systems based on the discrete wavelet packet transform (DWPT) and quantization index modulation (QIM) has been proposed that enables embedding of the watermark within an angle of the wavelet's sub-bands. To minimize the degradation effects of the watermark, these sub-bands were selected from frequency ranges where little speaker-specific information was available (500-3500 Hz and 6000-7000 Hz). Experimental results on the TIMIT, MIT, and MOBIO speech databases show that the degradation results for speaker verification and identification are 0.39 and 0.97 %, respectively, which are negligible. In addition, the proposed watermark technique can provide the appropriate fragility required for different signal processing operations.

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Speaker verification security improvement by means of speech watermarking

Cited by 37 publications

References 9 publications

Spoofing and countermeasures for speaker verification: A survey

Spoofing and countermeasures for speaker verification: A survey

On the study of replay and voice conversion attacks to text-dependent speaker verification

Semi-fragile digital speech watermarking for online speaker recognition

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