Cross-Domain Replay Spoofing Attack Detection Using Domain Adversarial Training

Replay spoofing attacks are a major threat for speaker verification systems. Although many anti-spoofing systems or countermeasures are proposed to detect dataset-specific replay attacks with promising performance, they generalize poorly when applied on unseen datasets. In this work, the cross-dataset scenario is treated as a domain-mismatch problem and dealt with using a domain adversarial training framework. Compared with previous approaches, features learned from this newly-designed architecture are more discriminative for spoofing detection, but more indistinguishable across different domains. Only labeled source-domain data and unlabeled target-domain data are required during the adversarial training process, which can be regarded as unsupervised domain adaptation. Experiments on the ASVspoof 2017 V.2 dataset as well as the physical access condition part of BTAS 2016 dataset demonstrate that a significant EER reduction of over relative 30% can be obtained after applying the proposed domain adversarial training framework. It is shown that our proposed model can benefit from a large amount of unlabeled target-domain training data to improve detection accuracy.