Research on Voiceprint Adversarial Detection of Improved Xception Network.

Adversarial attacks against speaker recognition models have attracted widespread attention and posed a serious threat to speaker recognition systems' security in recent years. A voiceprint adversarial sample detection model e_Xception is proposed to solve the problems of excessive parameter size and poor robustness of existing voiceprint adversarial sample detection methods. Xception is taken as the backbone network and embeds efficient channel attention (ECA) modules to fully extract speech features. A lightweight network model e_halfXception is designed to reduce parameters' number while still maintaining high accuracy by reasonably reducing the width of the network model. Finally, a high-frequency masked speech data enhancement strategy HF-Mask is proposed to improve the model's generalization. Experimental results demonstrate that high accuracy is achieved in the detection of six adversarial samples, FGSM, BIM, PGD, MI-FGSM, C&W and FAKEBOB, outperforming other detection methods, and the robustness of the model is investigated unknown attack algorithms, unknown target models, and unknown perturbation degrees, validating the model's generalization. [ABSTRACT FROM AUTHOR]