Replay anti-spoofing countermeasure based on data augmentation with post selection. (November 2020) - Zhao, Yuanjun; Togneri, Roberto; Sreeram, Victor
Highlights: Novel anti-spoofing countermeasure for replay attacks Speech features are generated by GANs with conditions A CNN based post selection process is proposed 3.26% and 8.05% EERs on the development and evaluation subsets of the ASVspoof 2017 V2.0 corpus are achieved Abstract: 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.