ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (original) (raw)
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ASVspoof 2019: a large-scale public database of synthetic, converted and replayed speech
2020
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as "presentation attacks." These vulnerabilities are generally unacceptable and call for spoofing countermeasures or "presentation attack detection" systems. In addition to impersonation, ASV systems are vulnerable to replay, speech synthesis, and voice conversion attacks. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks wit...
2015
Automatic speaker verification (ASV) offers a low-cost and flexible biometric solution to person authentication. While the reliability of ASV systems is now considered sufficient to support mass-market adoption, there are concerns that the technology is vulnerable to spoofing, also referred to as presentation attacks. Spoofing refers to an attack whereby a fraudster attempts to manipulate a biometric system by masquerading as another, enrolled person. On the other hand, speaker adaptation in speech synthesis and voice conversion techniques attempt to mimic a target speaker’s voice automatically, and hence present a genuine threat to ASV systems. The research community has responded to speech synthesis and voice conversion spoofing attacks 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; ASV systems remain vulnerable to spoofing, and a deeper understanding of spe...
ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
2015
An increasing number of independent studies have confirmed the vulnerability of automatic speaker verification (ASV) technology to spoofing. However, in comparison to that involving other biometric modalities, spoofing and countermeasure research for ASV is still in its infancy. A current barrier to progress is the lack of standards which impedes the comparison of results generated by different researchers. The ASVspoof initiative aims to overcome this bottleneck through the provision of standard corpora, protocols and metrics to support a common evaluation. This paper introduces the first edition, summaries the results and discusses directions for future challenges and research.
On the vulnerability of speaker verification to realistic voice spoofing
2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015
Automatic speaker verification (ASV) systems are subject to various kinds of malicious attacks. Replay, voice conversion and speech synthesis attacks drastically degrade the performance of a standard ASV system by increasing its false acceptance rates. This issue raised a high level of interest in the speech research community where the possible voice spoofing attacks and their related countermeasures have been investigated. However, much less effort has been devoted in creating realistic and diverse spoofing attack databases that foster researchers to correctly evaluate their countermeasures against attacks. The existing studies are not complete in terms of types of attacks, and often difficult to reproduce because of unavailability of public databases. In this paper we introduce the voice spoofing data-set of AVspoof, a public audio-visual spoofing database. AVspoof includes ten realistic spoofing threats generated using replay, speech synthesis and voice conversion. In addition, we provide a set of experimental results that show the effect of such attacks on current state-of-the-art ASV systems.
ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge
IEEE Journal of Selected Topics in Signal Processing
Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes the ASV Spoofing and Countermeasures (ASVspoof) initiative, which aims to fill this void. Through the provision of a common dataset, protocols, and metrics, ASVspoof promotes a sound research methodology and fosters technological progress. This paper also describes the ASVspoof 2015 dataset, evaluation, and results with detailed analyses. A review of post-evaluation studies conducted using the same dataset illustrates the rapid progress stemming from ASVspoof and outlines the need for further investigation. Priority future research directions are presented in the scope of the next ASVspoof evaluation planned for 2017.
Overview of BTAS 2016 speaker anti-spoofing competition
2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)
This paper provides an overview of the Speaker Antispoofing Competition organized by Biometric group at Idiap Research Institute for the IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2016). The competition used AVspoof database, which contains a comprehensive set of presentation attacks, including, (i) direct replay attacks when a genuine data is played back using a laptop and two phones (Samsung Galaxy S4 and iPhone 3G), (ii) synthesized speech replayed with a laptop, and (iii) speech created with a voice conversion algorithm, also replayed with a laptop. The paper states competition goals, describes the database and the evaluation protocol, discusses solutions for spoofing or presentation attack detection submitted by the participants, and presents the results of the evaluation.
Re-assessing the threat of replay spoofing attacks against automatic speaker verification
This paper reexamines the threat of spoofing or presentation attacks in the context of automatic speaker verification (ASV). While voice conversion and speech synthesis attacks present as erious threat, and have accordingly receivedag reat deal of attention in the recent literature, theyc an only be implemented with ah igh level of technical know-how. In contrast, the implementation of replay attacks require no specific expertise nor anys ophisticated equipment and thus theya rguably present a greater risk. The comparative threat of each attack is reexamined in this paper against six different ASV systems including astate-of-the-art iVector-PLDAsystem. Despite the lack of attention in the literature, experiments showthat low-effort replay attacks provoke higher levels of false acceptance than comparatively higher-effort spoofing attacks such as voice conversion and speech synthesis. Results therefore showt he need to refocus research effort and to develop countermeasures against replay attacks in future work. * The work of A. Janicki wassupported by the European Union in the framework of the European Social Fund through the WarsawUniversity of Technology Development Programme.
An assessment of automatic speaker verification vulnerabilities to replay spoofing attacks
Security and Communication Networks, 2016
This paper analyses the threat of replay spoofing or presentation attacks in the context of automatic speaker verification. As relatively high-technology attacks, speech synthesis and voice conversion, which have thus far received far greater attention in the literature, are probably beyond the means of the average fraudster. The implementation of replay attacks, in contrast, requires no specific expertise nor sophisticated equipment. Replay attacks are thus likely to be the most prolific in practice, while their impact is relatively under-researched. The work presented here aims to compare at a high level the threat of replay attacks with those of speech synthesis and voice conversion. The comparison is performed using strictly controlled protocols and with six different automatic speaker verification systems including a state-of-the-art iVector/probabilistic linear discriminant analysis system. Experiments show that low-effort replay attacks present at least a comparable threat to speech synthesis and voice conversion. The paper also describes and assesses two replay attack countermeasures. A relatively new approach based on the local binary pattern analysis of speech spectrograms is shown to outperform a competing approach based on the detection of far-field recordings.
Spoofing and countermeasures for speaker verification: A survey
Speech Communication, 2015
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.
2013
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.