1,534 research outputs found

    Cross-database evaluation of audio-based spoofing detection systems

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    Since automatic speaker verification (ASV) systems are highly vulnerable to spoofing attacks, it is important to develop mechanisms that can detect such attacks. To be practical, however, a spoofing attack detection approach should have (i) high accuracy, (ii) be well-generalized for practical attacks, and (iii) be simple and efficient. Several audio-based spoofing detection methods have been proposed recently but their evaluation is limited to less realistic databases containing homogeneous data. In this paper, we consider eight existing presentation attack detection (PAD) methods and evaluate their performance using two major publicly available speaker databases with spoofing attacks: AVspoof and ASVspoof. We first show that realistic presentation attacks (speech is replayed to PAD system) are significantly more challenging for the considered PAD methods compared to the so called `logical access' attacks (speech is presented to PAD system directly). Then, via a cross-database evaluation, we demonstrate that the existing methods generalize poorly when different databases or different types of attacks are used for training and testing. The results question the efficiency and practicality of the existing PAD systems, as well as, call for creation of databases with larger variety of realistic speech presentation attacks

    Impact of score fusion on voice biometrics and presentation attack detection in cross-database evaluations

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    Research in the area of automatic speaker verification (ASV) has been advanced enough for the industry to start using ASV systems in practical applications. However, these systems are highly vulnerable to spoofing or presentation attacks, limiting their wide deployment. Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing ASV systems for practical and attack-resistant solutions. To be practical, however, an attack detection should (i) have high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient. Several audio-based presentation attack detection (PAD) methods have been proposed recently but their evaluation was usually done on a single, often obscure, database with limited number of attacks. Therefore, in this paper, we conduct an extensive study of eight state-of-the-art PAD methods and evaluate their ability to detect known and unknown attacks (e.g., in a cross-database scenario) using two major publicly available speaker databases with spoofing attacks: AVspoof and ASVspoof. We investigate whether combining several PAD systems via score fusion can improve attack detection accuracy. We also study the impact of fusing PAD systems (via parallel and cascading schemes) with two i-vector and inter-session variability based ASV systems on the overall performance in both bona fide (no attacks) and spoof scenarios. The evaluation results question the efficiency and practicality of the existing PAD systems, especially when comparing results for individual databases and cross-database data. Fusing several PAD systems can lead to a slightly improved performance; however, how to select which systems to fuse remains an open question. Joint ASV-PAD systems show a significantly increased resistance to the attacks at the expense of slightly degraded performance for bona fide scenarios
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