  Speaker recognition is a popular topic in biometric authentication and many
deep learning approaches have achieved extraordinary performances. However, it
has been shown in both image and speech applications that deep neural networks
are vulnerable to adversarial examples. In this study, we aim to exploit this
weakness to perform targeted adversarial attacks against the x-vector based
speaker recognition system. We propose to generate inaudible adversarial
perturbations achieving targeted white-box attacks to speaker recognition
system based on the psychoacoustic principle of frequency masking.
Specifically, we constrict the perturbation under the masking threshold of
original audio, instead of using a common l_p norm to measure the
perturbations. Experiments on Aishell-1 corpus show that our approach yields up
to 98.5% attack success rate to arbitrary gender speaker targets, while
retaining indistinguishable attribute to listeners. Furthermore, we also
achieve an effective speaker attack when applying the proposed approach to a
completely irrelevant waveform, such as music.
