Spoofing Attacks on Speaker Verification Systems Based Generated Voice using Genetic Algorithm

Speaker verification has played a significant role in authentication with the booming development of smartphones and intelligent terminals in recent years. However, most speaker verification systems directly store the users original voiceprint template data (or called acoustic features). In this paper, we reveal the insecurity and sensitiveness of voiceprint template data by carrying out spoofing attacks on speaker verification systems using genetic algorithm. Meanwhile, multiple generation models based on different genetic algorithms (standard genetic algorithm, multiple population genetic algorithm) are proposed, but also the effects of these generation models are compared. Moreover, experimental results on state-of-the-art text-independent speaker verification techniques (such as i-vector, GMM-UBM) clearly demonstrate that our generated attack voice with leaked voiceprint template data can completely imitate users and pass the speaker verification.