Countermeasure to handle replay attacks in practical speaker verification systems

In this work, a novel countermeasure is proposed for protecting the speaker verification (SV) system to replay based spoofing attacks. The replay attacks refer to the attacks made with recorded speech of a particular speaker by playing them back to the system, claiming as an authentic speaker. On analyzing live and recorded speech examples, it was noted that the low frequency contents get suppressed in case of recorded speech which can be considered as a distinguishable characteristics. The proposed approach is based on creation of average spectral bitmap for live and record speech, that captures the difference of the two categories in the low frequency range. The spectral bitmap of the test speech is compared to the averaged spectral bitmap of live and recorded speech by cosine kernel distance for identification. Further it is compared to a contrast method based on Gaussian mixture model (GMM) technique, where mel-frequency cepstral coefficient (MFCC) features of live and recorded speech data are extracted and separate GMMs are trained for each category. MFCC features of test speech are taken and likelihood is computed with respect to live and recorded models for classification. The experimental setup for this work is made over telephone channel based text-independent SV framework with two types of recorded speech i.e. close and distant recording. The proposed countermeasure is found to handle the replay attacks in an impressive manner.