Evaluation of wolf attack for classified target on speaker verification systems

Impersonation attack is one of the major security issues of biometric authentication systems. Wolf attacks use a biometric sample such that the similarities between this sample and a number of templates are resulting in high false matches with these templates. In the conventional evaluation with the wolf attack probability (WAP), wolf attacks took advantage of vulnerabilities on the specific matching algorithms, and thereby high WAPs were achieved. However, in actual biometric authentication systems, their algorithm will be black boxes, and artificial samples will be refused by someones´s observation or liveness detection; therefore, wolf attacks do not always have theoretical WAPs. We focus on speaker verification systems, and propose a wolf attack that does not depend on matching algorithms and in which people cannot guess whether wolves are artifacts or not. Additionally, we show that the wolf attack became more efficient by creating a wolf for each gender.