In a biometric system, each biometric sample or template is typically associated with a single identity. However, recent research has demonstrated the possibility of generating "morph" biometric samples that can successfully match more than a single identity. Morph attacks are now recognized as a potential security threat to biometric systems. However, most morph attacks have been studied on biometric modalities operating in the image domain, such as face, fingerprint, and iris. In this preliminary work, we introduce Voice Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech samples that impersonate the voice characteristics of a pair of individuals. Our experiments evaluate the vulnerabilities of two popular speaker recognition systems, ECAPA-TDNN and x-vector, to VIM, with a success rate (MMPMR) of over 80% at a false match rate of 1% on the Librispeech dataset.

翻译：在生物特征识别系统中，每个生物特征样本或模板通常与单一身份相关联。然而，近期研究表明，有可能生成能够成功匹配多个身份的“变形”生物特征样本。变形攻击现已被公认为生物特征系统的潜在安全威胁。但迄今为止，大多数变形攻击研究集中于图像域的生物特征模态，例如人脸、指纹和虹膜。在本初步工作中，我们提出了语音身份变形（VIM）——一种基于语音的变形攻击方法，能够合成模仿一对个体语音特征的语音样本。我们的实验评估了两种主流说话人识别系统（ECAPA-TDNN和x-vector）对VIM攻击的脆弱性，在Librispeech数据集上，当误匹配率为1%时，成功率达到超过80%（MMPMR）。