Fraud Detection in Voice-based Identity Authentication Applications and Services

摘要

Keeping track of the multiple passwords, PINs, memorable dates and other authentication details needed to gain remote access to accounts is one of modern life's less appealing challenges. The employment of a voice-based verification as a biometric technology for both children and adults could be a good replacement to the old fashioned memory dependent procedure. Using voice for authentication could be beneficial in several application areas, including, security, protection, education, call-based and web-based services. Voice-based biometric applications are subject to different types of spoofing attacks. The most accessible and affordable type of spoofing for a voice-based biometrics system is a replay attack. Replay, which is to playback a pre-recorded speech sample, presents a genuine risk to automatic speaker verification technology. This work presents two architectures for detecting frauds caused by replay attacks in a voice-based biometrics authentication systems. Experimental results confirmed that obtained performances from both methods could further improve by applying a machine learning algorithm for performing fusion at the score level. The performance of both methods further improved by fusion using independent sources of scores in different architectures.