基于声音空间梯度的高稳健性击键识别方法

刘影; 韩康康; 钱志鸿

doi:10.11959/j.issn.1000-436x.2020095

您当前的位置：

首页 >

文章列表页 >

基于声音空间梯度的高稳健性击键识别方法

学术论文 | 更新时间：2024-06-05

- 基于声音空间梯度的高稳健性击键识别方法
- High-roubustness keystroke recognition method based on acoustic spatial gradient
- 通信学报 2020年41卷第5期页码：96-103
- 作者机构：
  
  1. 辽宁工程技术大学电子与信息工程学院，辽宁葫芦岛 125105
  2. 北京邮电大学电子工程学院，北京 100876
  3. 吉林大学通信工程学院，吉林长春 130022
- 作者简介：
  
  [ "刘影（1983- ），女，吉林大安人，博士，辽宁工程技术大学副教授，主要研究方向为无线定位、智能无线感知" ]
  [ "韩康康（1997- ），男，河南商丘人，北京邮电大学硕士生，主要研究方向为认知无线电、智能信息处理" ]
  [ "钱志鸿（1957- ），男，吉林长春人，博士，吉林大学教授、博士生导师，主要研究方向为基于物联网、D2D、Wi-Fi、RFID等的无线网络与通信技术" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1403300);辽宁省教育厅一般项目资金资助项目(LJ2017QL013);辽宁省博士启动基金资助项目(20170520098);辽宁省自然科学基金资助项目(2019-ZD-0038);辽宁省创新人才支持计划基金资助项目(LR2016045)
- DOI：10.11959/j.issn.1000-436x.2020095
  中图分类号： TP391
- 网络出版日期：2020-05，
  
  纸质出版日期：2020-05-25
- 稿件说明：
移动端阅览
刘影, 韩康康, 钱志鸿. 基于声音空间梯度的高稳健性击键识别方法[J]. 通信学报, 2020,41(5):96-103.

Ying LIU, Kangkang HAN, Zhihong QIAN. High-roubustness keystroke recognition method based on acoustic spatial gradient[J]. Journal on communications, 2020, 41(5): 96-103.
刘影, 韩康康, 钱志鸿. 基于声音空间梯度的高稳健性击键识别方法[J]. 通信学报, 2020,41(5):96-103. DOI： 10.11959/j.issn.1000-436x.2020095.

Ying LIU, Kangkang HAN, Zhihong QIAN. High-roubustness keystroke recognition method based on acoustic spatial gradient[J]. Journal on communications, 2020, 41(5): 96-103. DOI： 10.11959/j.issn.1000-436x.2020095.

摘要

针对声音信号受实际环境噪声影响引起耳蜗倒谱系数（CFCC）波动是导致击键内容识别率低的主要原因，研究相邻键CFCC之间的空间特征，建立基于点的CFCC空间梯度结构；在此基础上，在训练集和测试上研究CFCC空间梯度对击键内容识别的影响及确切邻域的点选取；最后，建立基于声音的高稳健性击键内容识别方法。在不同环境下进行的实验表明，所提CFCC空间梯度声音特征效果较好，识别准确率为96.15% 。

Abstract

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection

the spatial characteristics of adjacent between CFCC were studied

and the spatial gradient structure of CFCC based on points was established.On this basis

the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally

a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.

关键词

Keywords

references

张弛 . 神经网络算法在电磁泄露及脑电信息认知中的应用 [D ] . 北京:北京邮电大学 , 2018 .

ZHANG C . Application of neural network algorithm in electromagnetic [D ] . Beijing:Beijing University of Posts and Telecommunications , 2018 .

BAUER A O , . Some aspects of military line communications as deployed by the german armed forces prior to 1945 [C ] // In Proceedings of 5th Aunnual Colloquium,The History of Military Communications.[S.n.:s.l] . 1999 : 1 - 21 .

张南 . 计算机电磁信息泄露与视频信息认知研究 [D ] . 北京:北京邮电大学 , 2019 .

ZHANG N . Research on computer electromagnetic information leakage and video [D ] . Beijing:Beijing University of Posts and Telecommunications , 2019 .

ZHU T , MA Q , ZHANG S F , et al . Context-free attacks using keyboard acoustic emanations [C ] // ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2014 : 453 - 464 .

ABHISHEK S , SAXENA N . Keyboard emanations in remote voice calls:password leakage and noise(less) masking defense [C ] // ACM Conference on Data and Application Security and Privacy . New York:ACM Press , 2018 : 103 - 110 .

ASONOV D , AGRAWAL R . Keyboard acoustic emanations [C ] // IEEE Symposium on Security and Privacy . Piscataway:IEEE Press , 2004 : 3 - 11 .

COMPAGNO A , CONTI M , LAIN D , et al . Don’t skype ＆ type! Acoustic eavesdropping in voice-over-IP [C ] // ACM on Asia Conference on Computer and Communications Security . New York:ACM Press , 2017 : 703 - 715 .

DU H , YU Z W , XIAO D . Sensing keyboard input for computer activity recognition with a smartphone [C ] // ACM International Joint Conference on Pervasive and Ubiquitous Computing . New York:ACM Press , 2017 : 25 - 28 .

ZHENG X S , YANG S L , JIN N G , et al . Diva:distributed voronoi-based acoustic source localization with wireless sensornetworks [C ] // IEEE Conference on Computer Communications . Piscataway:IEEE Press , 2016 : 1 - 9 .

米建伟 , 方晓莉 , 仇原鹰 . 非平稳背景噪声下声音信号增强技术 [J ] . 仪器仪表学报 , 2017 , 38 ( 1 ): 17 - 38 .

MI J W , FANG X L , QIU Y Y . Enhancement technology for the audio signal with nonstationary background noise [J ] . Chinese Journal of Scientific Instrument , 2017 , 38 ( 1 ): 17 - 38 .

史燕燕 , 白静 . 融合CFCC和Teager能量算子倒谱参数的语音识别 [J ] . 计算机科学 , 2019 , 46 ( 5 ): 286 - 289 .

SHI Y Y , BAI J . Speech recognition combining CFCC and Teager energy operators cepstral coefficients [J ] . Computer Science , 2019 , 46 ( 5 ): 286 - 289 .

ZHANG J , ZUO L , GAO J W . Digital instruments recognition based on PCA-BP neural network [C ] // IEEE Information Technology,Networking,Electronic,and Automation Control Conference . Piscataway:IEEE Press , 2017 : 928 - 932 .

沈伟国 , 王巍 . 基于顽健线性判别分析的击键特征识别方法 [J ] . 通信学报 , 2017 , 38 ( Z2 ): 26 - 29 .

SHEN W G , WANG W . Keystroke features recognition based on stable linear discriminant analysis [J ] . Journal on Communications , 2017 , 38 ( Z2 ): 26 - 29 .

浏览量

354

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于阵列的神经网络水声通信信号多参数联合估计算法

基于神经网络的恶意DNS流量检测方法

基于可见光和射频融合的通信定位一体化系统

基于特征依赖图的源代码漏洞检测方法

基于改进CFCC特征提取的语种识别算法研究