Name
Title | ||
|---|---|---|
Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification |
Abstract | ||
|---|---|---|
In recent years, there has been a growing interest in developing countermeasures against non zero-effort attacks for speaker verification systems. Until now, the focus has been on logical access attacks, where the spoofed samples are injected into the system through a software-based process. This paper investigates a more realistic type of attack, referred to as physical access or presentation attacks, where the spoofed samples are presented as input to the microphone. To detect such attacks, we propose a binary classifier based approach that uses long-term spectral statistics as feature input. Experimental studies on the AVspoof database, which contains presentation attacks based on replay, speech synthesis and voice conversion, shows that the proposed approach can yield significantly low detection error rate with a linear classifier (half total error rate of 0.038%). Furthermore, an investigation on Interspeech 2015 ASVspoof challenge dataset shows that it is equally capable of detecting logical access attacks. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BIOSIG.2016.7736933 | 2016 International Conference of the Biometrics Special Interest Group (BIOSIG) |
Keywords | Field | DocType |
presentation attack detection,long term spectral statistics,trustworthy speaker verification,logical access attacks,binary classifier,speech synthesis,voice conversion | Speech synthesis,Binary classification,Spoofing attack,Computer science,Word error rate,Speech recognition,Software,Statistics,Linear classifier,Physical access,Microphone | Conference |
Volume | ISSN | ISBN |
P-260 | 1617-5468 | 978-1-5090-0780-6 |
Citations | PageRank | References |
6 | 0.53 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hannah Muckenhirn | 1 | 29 | 3.08 |
| Mathew Magimai-Doss | 2 | 516 | 54.76 |
| Sébastien Marcel | 3 | 1984 | 123.84 |