Spoofing attacks to i-vector based voice verification systems using statistical speech synthesis with additive noise and countermeasure

Abstract

Even though improvements in the speaker verification (SV) technology with i-vectors increased their real-life deployment, their vulnerability to spoofing attacks is a major concern. Here, we investigated the effectiveness of spoofing attacks with statistical speech synthesis systems using limited amount of adaptation data and additive noise. Experiment results show that effective spoofing is possible using limited adaptation data. Moreover, the attacks get substantially more effective when noise is intentionally added to synthetic speech. Training the SV system with matched noise conditions does not alleviate the problem. We propose a synthetic speech detector (SSD) that uses session differences in i-vectors for counterspoofing. The proposed SSD had less than 0.5% total error rate in most cases for the matched noise conditions. For the mismatched noise conditions, missed detection rate further decreased but total error increased which indicates that some calibration is needed for mismatched noise conditions.