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The potential for biometric systems to be manipulated through some form of subversion is well acknowledged. One such approach known as spoofing relates to the provocation of false accepts in authentication applications. Another approach referred to as obfuscation relates to the provocation of missed detections in surveillance applications. While the automatic speaker verification research community is now addressing spoofing and countermeasures, vulnerabilities to obfuscation remain largely unknown. This paper reports the first study. Our work with standard NIST datasets and protocols shows that the equal error rate of a standard GMM-UBM system is increased from 9% to 48% through obfuscation, whereas that of a state-of-the-art i-vector system increases from 3% to 20%. We also present a generalised approach to obfuscation detection which succeeds in detecting almost all attempts to evade detection.

This paper presents the first investigation of evasion and obfuscation in the context of speaker recognition surveillance and forensics. In contrast to spoofing, which aims to provoke false acceptances in authentication applications, evasion and obfuscation target detection and recognition modules in order to provoke missed detections. The paper presents our analysis of each vulnerability and the potential for countermeasures using standard NIST datasets and protocols and six different speaker recognition systems (from a standard GMM-UBM system to a state-of-the-art i-vector system). Results show that all systems are vulnerable to both evasion and obfuscation attacks and that a new generalised countermeasure shows promising detection performance. While all evasion attacks and almost all obfuscation attacks are detected in the case of this particular setup, the work nonetheless highlights the need for further research.

Voice conversion is a process which converts or transforms one speaker’s voice towards that of another. The literature shows that voice conversion can be used to spoof or fool an automatic speaker verification system. State-of-the-art voice conversion algorithms can produce high-quality speech signals in real time and are capable of fooling both human listeners and automatic systems, including text-independent and text-dependent. Furthermore, since converted voice originates from a living person, traditional liveness detection countermeasures are not necessarily effective in detecting such attacks. With today’s state-of-the-art algorithms producing high-quality speech with only few indicative processing artifacts, the detection of converted voice can be especially challenging.