Log In Sign Up

Replay attack spoofing detection system using replay noise by multi-task learning

by   Hyejin Shim, et al.

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


page 1

page 2

page 3

page 4


Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes

In this paper, we propose a replay attack spoofing detection system for ...

A study on the role of subsidiary information in replay attack spoofing detection

In this study, we analyze the role of various categories of subsidiary i...

Integrated Replay Spoofing-aware Text-independent Speaker Verification

A number of studies have successfully developed speaker verification or ...

Multi-Task Siamese Neural Network for Improving Replay Attack Detection

Automatic speaker verification systems are vulnerable to audio replay at...

Self-supervised pre-training with acoustic configurations for replay spoofing detection

Large datasets are well-known as a key to the recent advances in deep le...

Discriminate natural versus loudspeaker emitted speech

In this work, we address a novel, but potentially emerging, problem of d...