TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Robust Method for Speech Replay Attack Detection |
| 영문제목(English Title) |
A Robust Method for Speech Replay Attack Detection |
| 저자(Author) |
Lang Lin
Rangding Wang
Diqun Yan
Li Dong
|
| 원문수록처(Citation) |
VOL 14 NO. 01 PP. 0168 ~ 0182 (2020. 01) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Spoofing attacks, especially replay attacks, pose great security challenges to automatic speaker verification (ASV) systems. Current works on replay attacks detection primarily focused on either developing new features or improving classifier performance, ignoring the effects of feature variability, e.g., the channel variability. In this paper, we first establish a mathematical model for replay speech and introduce a method for eliminating the negative interference of the channel. Then a novel feature is proposed to detect the replay attacks. To further boost the detection performance, four post-processing methods using normalization techniques are investigated. We evaluate our proposed method on the ASVspoof 2017 dataset. The experimental results show that our approach outperforms the competing methods in terms of detection accuracy. More interestingly, we find that the proposed normalization strategy could also improve the performance of the existing algorithms.
|
| 키워드(Keyword) |
Automatic speaker verification
replay attacks
channel effect
robustness
post-processing
|
| 파일첨부 |
PDF 다운로드
|
