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Replay anti-spoofing countermeasure based on data augmentation with post selection

机译：基于数据增强的帖子选择重新播放反欺骗对策

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摘要

Automatic Speaker Verification (ASV) systems have been widely applied for speaker authentication for biometric security especially in e-business scenarios. However, vulnerabilities of the ASV technology have been discovered and have generated much interest to design anti-spoofing countermeasures. Serious threats can be posed by replay attacks, which are difficult to detect and easy to mount with accessible devices. In this paper, an efficient replay anti-spoofing countermeasure based on data augmentation with post selection is proposed. The auxiliary classifier generative adversarial network (AC-GAN) is adopted to generate more speech samples with diverse variants. To select generated samples of high quality and avoid the bias caused by human subjective perception, we also propose a convolutional neural network (CNN) based post-filter. By integrating data augmentation and post selection approaches, issues of over-fitting and lack of generalization can be significantly alleviated with extra informative training data. The proposed anti-spoofing countermeasure is evaluated on the ASVspoof 2017 Version 2.0 database. Experimental results measured by equal error rates (EERs) indicate a promising improvement over the development and evaluation subsets. This provides the motivation for novel audio data augmentation and also promotes the future research on generation selection in the application of speaker spoofing detection.
机译：自动扬声器验证（ASV）系统已被广泛应用于生物识别安全性的扬声器认证，尤其是在电子商务方案中。然而，已经发现了ASV技术的漏洞，并为设计防欺骗对策产生了很多兴趣。重播攻击可以构成严重威胁，这很难检测和易于安装可访问设备。在本文中，提出了一种基于数据增强的有效重播防欺骗对策。采用辅助分类器生成的对抗网络（AC-GAN）来产生具有不同变体的更多语音样本。选择高质量的生成样本并避免由人类主观感知引起的偏差，我们还提出了一种基于过滤器的卷积神经网络（CNN）。通过整合数据增强和后选择方法，可以通过额外的信息培训数据显着减轻过度拟合和缺乏的概率问题。建议的反欺骗对策在ASVSpoof 2017版本2.0数据库上进行评估。通过等于误差率（EERS）测量的实验结果表明对开发和评估子集的有望改善。这提供了新颖音频数据增强的动机，并且还促进了扬声器欺骗检测应用中的生成选择的未来研究。

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