Paper Info
Title
Replay spoofing attack detection using deep neural networks.
Abstract
In recent years, there has been increased interest in speaker verification(SV) systems and their usage has become widespread. This situation made the detecting of spoofing attacks, the discrimination of genuine speech from spoofed speech, an important research area for speaker verification (SV) systems. In this study, detection of replay spoofing attacks where a pre-recorded speech signal is used to gain unauthorized access to ASV systems is studied. Mel frequency cepstral coefficients (MFCC) and long-term average spectrum (LTAS) statistics features are used to detect replay attacks using deep neural network (DNN) classifier. Experimental results using ASVspoof 2017 database show that MFCC and LTAS features with DNN classifier out-performs the Gaussian mixture model (GMM) classifier with constant Q transform cepstral coefficients (CQCC) which is the baseline replay attack detection system of the ASVspoof 2017 challenge.
Year
Venue
Keywords
2018
Signal Processing and Communications Applications Conference
speaker verification,spoofing attacks,anti-spoofing,deep neural networks
Field
DocType
ISSN
Constant Q transform,Mel-frequency cepstrum,Pattern recognition,Spoofing attack,Computer science,Feature extraction,Artificial intelligence,Classifier (linguistics),Artificial neural network,Replay attack,Mixture model
Conference
2165-0608
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Bekir Bakar100.68
Cemal Hanilçi217111.23