Digital speech watermarking for anti-spoofing attack in speaker recognition

Nematollahi, Mohammad Ali; Al-Haddad, S. A. R.; Doraisamy, Shyamala; Ranjbari, M.

doi:10.1109/tenconspring.2014.6863080

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Although there has been much research on these technologies for use in various applications (e.g., for assisting individuals with vocal disabilities), they can also be used for vocal identity falsification such as in spoofing attacks against ASV systems, representing a serious personal security issue [8,9,10]. This has led to the recent emergence of research on the definition and development of countermeasures for detecting spoofing attacks [11,12,13,14]. Typically, three different types of these attacks are considered: replay, speech synthesis, and voice conversion.…”

Section: Introductionmentioning

confidence: 99%

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Shiota¹,

Villavicencio²,

Yamagishi³

et al. 2016

The Speaker and Language Recognition Workshop (Odyssey 2016)

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This paper presents an algorithm for detecting spoofing attacks against automatic speaker verification (ASV) systems. While such systems now have performances comparable to those of other biometric modalities, spoofing techniques used against them have progressed drastically. Several techniques can be used to generate spoofing materials (e.g., speech synthesis and voice conversion techniques), and detecting them only on the basis of differences at an acoustic speaker modeling level is a challenging task. Moreover, differences between "live" and artificially generated material are expected to gradually decrease in the near future due to advances in synthesis technologies. A previously proposed "voice liveness" detection framework aimed at validating whether speech signals were generated by a person or artificially created uses elementary algorithms to detect pop noise. Detection is taken as evidence of liveness. A more advanced detection algorithm has now been developed that combines single-and double-channel pop noise detection. Experiments demonstrated that this tandem algorithm detects pop noise more effectively: the detection error rate was up to 80% less that those achieved with the elementary algorithms.

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Section: Introductionmentioning

confidence: 99%

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Shiota¹,

Villavicencio²,

Yamagishi³

et al. 2016

The Speaker and Language Recognition Workshop (Odyssey 2016)

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show abstract

“…However, such technologies can be used to falsify profiles or identities and perform spoofing attacks against ASV systems, representing a serious challenge to the successful operation of these systems [9][10][11]. Research on the definition and development of countermeasures for the detection of spoofing attacks already exists [12][13][14][15]. Conventionally, attacks of three different natures are considered: replay, speech synthesis, and voice conversion.…”

Section: Introductionmentioning

confidence: 99%

Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification

Shiota

Villavicencio

Yamagishi³

et al. 2015

Interspeech 2015

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This paper proposes a novel countermeasure framework to detect spoofing attacks to reduce the vulnerability of automatic speaker verification (ASV) systems. Recently, ASV systems have reached equivalent performances equivalent to those of other biometric modalities. However, spoofing techniques against these systems have also progressed drastically. Experimentation using advanced speech synthesis and voice conversion techniques has showed unacceptable false acceptance rates and several new countermeasure algorithms have been explored to detect spoofing materials accurately. However, the countermeasures proposed so far are based on the acoustic differences between natural speech signals and artificial speech signals, expected to become gradually smaller in the near future. In this paper, we focus on voice liveness detection, which aims to validate whether the presented speech signals originated from a live human. We use the phenomenon of pop noise, which is a distortion that happens when human breath reaches a microphone, as liveness evidence. This paper proposes pop noise detection algorithms and shows through an experimental study that they can be used to discriminate live voice signals from artificial ones generated by means of speech synthesis techniques.

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“…Eight potential cracks are available which made online speaker recognition systems vulnerable [2]. Recently, speech watermarking is used to secure the communication channel against intentional and unintentional attacks for speaker verification and identification purpose [3][4][5][6][7]. For this reason, the watermark is embedded to verify the authenticity of the transmitter (i.e., sensor and feature extractors) and the integrity of the entire authentication mechanism.…”

Section: Introductionmentioning

confidence: 99%

Robust Digital Speech Watermarking For Online Speaker Recognition

Nematollahi

Gamboa-Rosales

Akhaee

et al. 2015

Mathematical Problems in Engineering

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A robust and blind digital speech watermarking technique has been proposed for online speaker recognition systems based on Discrete Wavelet Packet Transform (DWPT) and multiplication to embed the watermark in the amplitudes of the wavelet’s subbands. In order to minimize the degradation effect of the watermark, these subbands are selected where less speaker-specific information was available (500 Hz–3500 Hz and 6000 Hz–7000 Hz). Experimental results on Texas Instruments Massachusetts Institute of Technology (TIMIT), Massachusetts Institute of Technology (MIT), and Mobile Biometry (MOBIO) show that the degradation for speaker verification and identification is 1.16% and 2.52%, respectively. Furthermore, the proposed watermark technique can provide enough robustness against different signal processing attacks.

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Digital speech watermarking for anti-spoofing attack in speaker recognition

Cited by 6 publications

References 11 publications

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification

Robust Digital Speech Watermarking For Online Speaker Recognition

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