数据采集与处理2024，Vol.39Issue(2)：416-423,8.DOI:10.16337/j.1004-9037.2024.02.014
基于自注意力机制的音频对抗样本生成方法
Audio Adversarial Examples Generation Method Based on Self-attention Mechanism
李珠海 1郭武1
作者信息
- 1. 中国科学技术大学语音及语言信息处理国家工程研究中心,合肥 230027
- 折叠
摘要
Abstract
With the widespread of personal speech and development of automatic speaker recognition algorithms,personal privacy protection is in a high-risk situation.Audio adversarial examples can protect personal voiceprint features through disabling automatic speaker recognition algorithms while the subjective hearing of the human ear remains unchanged.We improve the typical adversarial attacks algorithm FoolHD with multi-head self-attention mechanism,and we call it FoolHD-MHSA.First,convolutional neural networks are introduced as the encoder to extract adversarial perturbation spectrograms.Second,we use self-attention mechanism to extract correlation features of different parts of perturbation spectrogram from a global perspective,focus the network on the important information and suppress the useless information.Finally,the processed perturbation spectrogram is steganographed into the input spectrogram with a decoder to get adversarial example spectrogram.Experimental results show that FoolHD-MHSA can generate adversarial examples with higher attack success rate and average PESQ score than FoolHD.关键词
自注意力机制/对抗样本/说话人识别/深度神经网络Key words
self-attention mechanism/adversarial examples/speaker recognition/deep neural network分类
信息技术与安全科学引用本文复制引用
李珠海,郭武..基于自注意力机制的音频对抗样本生成方法[J].数据采集与处理,2024,39(2):416-423,8.
