Vulnerability of speaker verification systems against voice conversion spoofing attacks: The case of telephone speech

Kinnunen, Tomi; Wu, Zhizheng; Lee, Kong Aik; Sedlak, Filip; Chng, Eng Siong; Li, Haizhou

doi:10.1109/icassp.2012.6288895

Cited by 146 publications

(116 citation statements)

References 13 publications

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“…Similar to many other studies [12,22], spoofing detection was evaluated by measuring the EER values. In addition, detection error trade-off (DET) curves were plotted to show the relation between false alarm and miss probabilities.…”

Section: Resultsmentioning

confidence: 99%

“…Quite recently, researchers have started to investigate how much ASV systems are prone to spoofing using various methods. Various researchers have worked on assessing the threat caused by imitators [13,23], speech synthesis [26,35], converted speech [22,39] or replay of previously acquired recordings [3,24]. In parallel, much effort has been invested in work on various spoofing countermeasures.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Increasing anti-spoofing protection in speaker verification using linear prediction

Janicki

2016

Multimed Tools Appl

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“…Second, the voice conversion (VC) techniques (Jin et al, 2008), (Kinnunen et al, 2012). Both techniques can be used to generate spoofing signals that can successfully deceive state-of-the-art SV systems with false acceptance rates (FAR) around 80% for synthetic speech and 5% for VC.…”

Section: Introductionmentioning

confidence: 99%

Synthetic speech detection using phase information

Saratxaga

Sánchez

et al. 2016

Speech Communication

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Taking advantage of the fact that most of the speech processing techniques neglect the phase information, we seek to detect phase perturbations in order to prevent synthetic impostors attacking Speaker Verification systems. Two Synthetic Speech Detection (SSD) systems that use spectral phase related information are reviewed and evaluated in this work: one based on the Modified Group Delay (MGD), and the other based on the Relative Phase Shift, (RPS). A classical modulebased MFCC system is also used as baseline. Different training strategies are proposed and evaluated using both real spoofing samples and copy-synthesized signals from the natural ones, aiming to alleviate the issue of getting real data to train the systems. The recently published ASVSpoof2015 database is used for training and evaluation. Performance with completely unrelated data is also checked using synthetic speech from the Blizzard Challenge as evaluation material. The results prove that phase information can be successfully used for the SSD task even with unknown attacks.

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“…Impersonation, replay, speech synthesis and voice conversion are some examples of spoofing attacks. Impersonation or human mimicking requires a mimic to imitate a target speaker's voice and it does not pose a genuine threat to automatic speaker verification system [1][2][3][4][5]. Replay attacks consist of playing back the pre-recorded voice of a target speaker to spoof the system.…”

Section: Introductionmentioning

confidence: 99%

Spoofing detection employing infinite impulse response — constant Q transform-based feature representations

Alam

Kenny

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Speaker recognition researchers acknowledge that systems which aim to verify speakers automatically based on their pronunciation of an utterance are vulnerable to spoofing attacks using voice conversion and speech synthesis technologies. The first automatic speaker verification spoofing and countermeasures challenge (ASVspoof2015) was designed to stimulate interest in this problem among the speaker recognition communities. In the course of the challenge and subsequently, it became clear that the most effective countermeasures against spoofing attacks are low-level acoustic features (typically extracted at 10 ms intervals) designed to detect artifacts in synthetic or voice converted speech. In this work, we demonstrate the effectiveness of the infinite impulse responseconstant Q transform (IIR-CQT) spectrum-based cepstral coefficients (ICQC) as anti-spoofing front-end. The IIR-CQT spectrum is estimated by filtering the multi-resolution fast Fourier transform with an infinite impulse response filter. These features can be used on their own with a standard Gaussian mixture model backend to detect spoofing attacks or they can be used in tandem with bottleneck features which are extracted from a bottleneck layer in a deep neural network designed to discriminate between synthetic and natural speech. We show that the ICQC features are capable of producing very low equal error rates on the individual spoofing attacks in the ASVspoof2015 data set (0.02% on the known attacks, 0.23% on the unknown attacks, and 0.13% on average). Moreover, with a single decision threshold (common to all of the attacks), the ICQC front end yielded an equal error rate of 0.20%.

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Vulnerability of speaker verification systems against voice conversion spoofing attacks: The case of telephone speech

Cited by 146 publications

References 13 publications

Increasing anti-spoofing protection in speaker verification using linear prediction

Increasing anti-spoofing protection in speaker verification using linear prediction

Synthetic speech detection using phase information

Spoofing detection employing infinite impulse response — constant Q transform-based feature representations

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