You Can Hear But You Cannot Steal: Defending against Voice Impersonation Attacks on Smartphones

被引:82
作者
Chen, Si [1 ,2 ]
Ren, Kui [1 ]
Piao, Sixu [1 ]
Wang, Cong [3 ]
Wang, Qian [4 ]
Weng, Jian [5 ]
Su, Lu [1 ]
Mohaisen, Aziz [1 ]
机构
[1] SUNY Buffalo, Dept Comp Sci & Engn, Buffalo, NY 14620 USA
[2] West Chester Univ, Dept Comp Sci, W Chester, PA 19382 USA
[3] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
[4] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China
[5] Jinan Univ, Sch Informat Sci & Technol, Guangzhou, Guangdong, Peoples R China
来源
2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017) | 2017年
基金
美国国家科学基金会; 新加坡国家研究基金会;
关键词
SPEAKER RECOGNITION;
D O I
10.1109/ICDCS.2017.133
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Voice, as a convenient and efficient way of information delivery, has a significant advantage over the conventional keyboard-based input methods, especially on small mobile devices such as smartphones and smartwatches. However, the human voice could often be exposed to the public, which allows an attacker to quickly collect sound samples of targeted victims and further launch voice impersonation attacks to spoof those voice-based applications. In this paper, we propose the design and implementation of a robust software-only voice impersonation defense system, which is tailored for mobile platforms and can be easily integrated with existing off-the-shelf smart devices. In our system, we explore magnetic field emitted from loudspeakers as the essential characteristic for detecting machine-based voice impersonation attacks. Furthermore, we use a state-of-the-art automatic speaker verification system to defend against human imitation attacks. Finally, our evaluation results show that our system achieves simultaneously high accuracy (100%) and low equal error rates (EERs) (0%) in detecting the machine-based voice impersonation attack on smartphones.
引用
收藏
页码:183 / 195
页数:13
相关论文
共 48 条
  • [1] Adami A., 2003, ICASSP
  • [2] Alegre F., 2013, ICASSP
  • [3] Alegre Federico., 2012, EUSIPCO
  • [4] Amin Talal B., 2013, Journal of the Acoustical Society of America, V134, DOI 10.1121/1.4830853
  • [5] [Anonymous], VOIC
  • [6] [Anonymous], ACM WORKSH SEC PRIV
  • [7] [Anonymous], 2012, MOBICOM
  • [8] Baidu, VOIC BIOM SMARTPH
  • [9] Glottal and Vocal Tract Characteristics of Voice Impersonators
    Bin Amin, Talal
    Marziliano, Pina
    German, James Sneed
    [J]. IEEE TRANSACTIONS ON MULTIMEDIA, 2014, 16 (03) : 668 - 678
  • [10] Speaker recognition: A tutorial
    Campbell, JP
    [J]. PROCEEDINGS OF THE IEEE, 1997, 85 (09) : 1437 - 1462