Attack Towards Speaker Identification Using Deep Conversion Networks for Voiceprint Features
In the field of speaker identification(SID)systems,attacks often rely on fast gra-dient descent and mapping gradient descent algorithms,which suffer from unstable attack performance and poor auditory quality of generated attack samples.This paper proposes an advanced attack method against SID systems using deep neural networks to generate attack speeches with the target speaker's voiceprint.Specifically,the attack process on SID system is first analyzed to determine the approach to generating attack speeches.Then,a two-dimensional convolutional neural network is designed as a generator to effectively integrate the speech content of the source speaker and the voiceprint features of the target speaker.A discriminator is designed based on adversarial learning to improve the quality of the attack speeches.Finally,comparative experiments are conducted on two automatic SID systems based on generalized end-to-end loss and AMSoftmax loss,respectively.Ex-perimental results demonstrate that the proposed method not only improves the stability of attack performance,but also enhances the auditory quality of attack speeches.Moreover,the proposed method is applicable to short samples,making it suitable for practical attack scenarios.
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