Replay attack spoofing detection system using replay noise by multi-task learning
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In this paper, we propose a spoofing detection system for replay attack using replay noise. In many previous studies across various domains, noise has been reduced. However, in replay attack, we hypothesize that noise is the prominent feature which is different with original signal and it can be one of the keys to find whether a signal has been spoofed. We define the noise that is caused by the replay attack as replay noise. Specifically, the noise of playback devices, recording environments, and recording devices, is included in the replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. Multi-task learning was exploited to embed spoofing detection and replay noise classification in the code layer. The experiment results on the ASVspoof2017 datasets demonstrate that the performance of our proposed system is relatively improved 30
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