说话人验证系统攻击方法的研究现状及展望
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陆军工程大学指挥控制工程学院,南京 210007

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国家自然科学基金(62071484)资助项目;江苏省优秀青年基金(BK20180080)资助项目。


Attack Methods in Speaker Verification System:The State of the Art and Prospects
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College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China

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    摘要:

    自动说话人验证(Automatic speaker verification,ASV)技术的发展正在深刻地影响和改变着当前的人机交互系统,ASV作为一些智能设备的语音核心功能,可以接受目标说话人的语音并准确识别出该说话人的身份。近年来,人工智能技术的快速进展推动了ASV系统实现跨越式发展。然而,随着人工神经网络和深度学习技术的发展,越来越多的研究者开始研究如何攻击ASV系统。如何通过对原始语音进行一系列处理实现对ASV系统的攻击,是近年来语音领域研究的一个热点问题。目前,对ASV系统的攻击方法大致可分为欺骗攻击(Spoofing attack)和对抗攻击(Adversarial attack)两大类。本文对两大类的典型方法和基本原理进行综述,梳理了目前一些攻击手段中存在的若干问题,揭示了ASV系统存在的安全隐患,对今后ASV系统安全性的发展做了简要的展望,并为未来进一步提高ASV系统的安全性和可靠性提供了参考。

    Abstract:

    The development of automatic speaker verification (ASV) technology is profoundly affecting and changing the current human-computer interaction system. As the core speech function of some smart devices, ASV can accept the voice of the target speakers and accurately identify the speakers’ identities. In recent years, the rapid development of artificial intelligence technology has promoted the leapfrog development of ASV systems. However, with the development of artificial neural network and deep learning technology, more and more researchers begin to study the way to attack ASV systems. How to attack ASV systems through a series of processing of raw speech has been a hot topic in speech research in recent years. At present, the attack methods of ASV systems can be roughly divided into spoofing attacks and adversarial attacks. In this paper, the typical methods and basic principles of the two kinds of attacks are summarized, some problems existing in current attack methods are sorted out, the safety problems existing in the system of ASV are revealed, a brief outlook on the future development of ASV system security is given, and the development directions of improving the security and reliability of ASV systems are provided.

    图1 欺骗攻击和对抗攻击Fig.1 Spoofing attack and adversarial attack
    图2 典型的ASV系统Fig.2 Typical ASV
    图3 GMM/i-vector系统Fig.3 GMM/i-vector framework
    图4 DNN-UBM/i-vector系统Fig.4 DNN-UBM/i-vector framework
    图5 DNN-BNF/i-vector框架Fig.5 DNN-BNF/i-vector framework
    图6 d-vector框架Fig.6 d-vector framework
    图7 x-vector框架Fig.7 x-vector framework
    图8 语音重放场景Fig.8 Replay attack scenario
    图9 语谱图NMFFig.9 Non-negative matrix factorization of spectrogram
    图10 CycleGAN示意图Fig.10 Schematic diagram of CycleGAN
    图11 TTS流程图Fig.11 Flow chart of TTS
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张雄伟,张星昱,孙蒙,邹霞.说话人验证系统攻击方法的研究现状及展望[J].数据采集与处理,2021,36(5):831-849

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  • 收稿日期:2021-04-01
  • 最后修改日期:2021-07-07
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  • 在线发布日期: 2021-10-22