research-article

VoiceLive: A Phoneme Localization based Liveness Detection for Voice Authentication on Smartphones

Authors:

Yingying ChenAuthors Info & Claims

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

Pages 1080 - 1091

https://doi.org/10.1145/2976749.2978296

Published: 24 October 2016 Publication History

Abstract

Voice authentication is drawing increasing attention and becomes an attractive alternative to passwords for mobile authentication. Recent advances in mobile technology further accelerate the adoption of voice biometrics in an array of diverse mobile applications. However, recent studies show that voice authentication is vulnerable to replay attacks, where an adversary can spoof a voice authentication system using a pre-recorded voice sample collected from the victim. In this paper, we propose VoiceLive, a practical liveness detection system for voice authentication on smartphones. VoiceLive detects a live user by leveraging the user's unique vocal system and the stereo recording of smartphones. In particular, with the phone closely placed to a user's mouth, it captures time-difference-of-arrival (TDoA) changes in a sequence of phoneme sounds to the two microphones of the phone, and uses such unique TDoA dynamic which doesn't exist under replay attacks for liveness detection. VoiceLive is practical as it doesn't require additional hardware but two-channel stereo recording that is supported by virtually all smartphones. Our experimental evaluation with 12 participants and different types of phones shows that VoiceLive achieves over 99% detection accuracy at around 1% Equal Error Rate (EER). Results also show that VoiceLive is robust to different phone placements and is compatible to different sampling rates and phone models.

References

[1]

Android voice recognition. http://www.popsci.com/new-android-can-recognize-your-voice.

[2]

Google smart lock. https://get.google.com/smartlock/.

[3]

Hsbc offers voice biometric. http://www.bbc.com/news/business-35609833.

[4]

Mobile voice biometric security. http://voicevault.com/hsbc-embraces-mobile-voice-biometric-security-technology/.

[5]

Saypay technologies. http://saypaytechnologies.com/.

[6]

Vocalpassword. http://www.nuance.com/ucmprod/groups/enterprise/@web-enus/documents/collateral/nc_015226.pdf.

[7]

Voicekey mobile applications. http://speechpro-usa.com/product/voice_authentication/voicekey#tab2.

[8]

Wechat voiceprint. http://blog.wechat.com/2015/05/21/voiceprint-the-new-wechat-password/.

[9]

F. Alegre, A. Amehraye, and N. Evans. A one-class classification approach to generalised speaker verification spoofing countermeasures using local binary patterns. In IEEE BTAS, 2013.

[10]

A. Aley-Raz, N. M. Krause, M. I. Salmon, and R. Y. Gazit. Device, system, and method of liveness detection utilizing voice biometrics, May 14 2013. US Patent 8,442,824.

[11]

T. B. Amin, J. S. German, and P. Marziliano. Detecting voice disguise from speech variability: Analysis of three glottal and vocal tract measures. The Journal of the Acoustical Society of America, 2013.

[12]

L.-W. Chen, W. Guo, and L.-R. Dai. Speaker verification against synthetic speech. In 2010 IEEE Chinese Spoken Language Processing (ISCSLP), 2010.

[13]

G. Chetty and M. Wagner. Automated lip feature extraction for liveness verification in audio-video authentication. Proc. Image and Vision Computing, 2004.

[14]

P. L. De Leon, M. Pucher, J. Yamagishi, I. Hernaez, and I. Saratxaga. Evaluation of speaker verification security and detection of hmm-based synthetic speech. IEEE Processing of Audio, Speech, and Language, 2012.

Digital Library

[15]

E. Hall. Handbook for proxemic research. Anthropology News, 1995.

[16]

R. G. Hautamaki, T. Kinnunen, V. Hautamaki, T. Leino, and A.-M. Laukkanen. I-vectors meet imitators: on vulnerability of speaker verification systems against voice mimicry. In INTERSPEECH, 2013.

[17]

A. Jain, R. Bolle, and S. Pankanti. Biometrics: personal identification in networked society. Springer Science & Business Media, 2006.

Digital Library

[18]

T. Kevenaar. Protection of biometric information. In Security with Noisy Data. 2007.

[19]

T. Kinnunen et al. Vulnerability of speaker verification systems against voice conversion spoofing attacks: The case of telephone speech. In IEEE ICASSP, 2012.

[20]

A. Kipp, M.-B. Wesenick, and F. Schiel. Automatic detection and segmentation of pronunciation variants in german speech corpora. In IEEE ICSLP, 1996.

[21]

T. Kisler, F. Schiel, and H. Sloetjes. Signal processing via web services: the use case webmaus. In Digital Humanities Conference, 2012.

[22]

C. H. Knapp and G. C. Carter. The generalized correlation method for estimation of time delay. IEEE Processing of Acoustics, Speech and Signal, 1976.

[23]

P. Ladefoged. A course in phonetics. Hardcourt Brace Jovanovich Inc. NY, 2014.

[24]

J. Lindberg, M. Blomberg, et al. Vulnerability in speaker verification-a study of technical impostor techniques. In Eurospeech, 1999.

[25]

J. Liu, Y. Wang, G. Kar, Y. Chen, J. Yang, and M. Gruteser. Snooping keystrokes with mm-level audio ranging on a single phone. In ACM MobiCom, 2015.

Digital Library

[26]

D. Mukhopadhyay, M. Shirvanian, and N. Saxena. All your voices are belong to us: Stealing voices to fool humans and machines. In European Symposium on Research in Computer Security, 2015.

[27]

J. P. Olive, A. Greenwood, and J. Coleman. Acoustics of American English speech: a dynamic approach. Springer Science & Business Media, 1993.

[28]

J. C. Poss, D. Boye, and M. W. Mobley. Biometric voice authentication, June 10 2008. US Patent 7,386,448.

[29]

M. K. Ravishankar. Efficient algorithms for speech recognition. Technical report, DTIC Document, 1996.

[30]

M. A. Redford. The handbook of speech production. John Wiley & Sons, 2015.

[31]

W. Shang and M. Stevenson. Score normalization in playback attack detection. In IEEE ICASSP, 2010.

[32]

J. Villalba and E. Lleida. Detecting replay attacks from far-field recordings on speaker verification systems. In Biometrics and ID Management. 2011.

Digital Library

[33]

Z.-F. Wang, G. Wei, and Q.-H. He. Channel pattern noise based playback attack detection algorithm for speaker recognition. In IEEE ICMLC, 2011.

[34]

Z. Wu, T. Kinnunen, E. Chng, and H. Li. A study on spoofing attack in state-of-the-art speaker verification: the telephone speech case. In IEEE APSIPA ASC, 2012.

[35]

J. Yang, Y. Chen, and W. Trappe. Detecting spoofing attacks in mobile wireless environments. In SECON, 2009.

Digital Library

Cited By

Yang YWang YChen YYe ZLi XXia ZRen YOkoshi TKo JLiKamWa R(2024)TouchTone: Smartwatch Privacy Protection via Unobtrusive Finger Touch GesturesProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661884(141-154)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661884
Duan DSun ZNi TLi SJia XXu WLi TOkoshi TKo JLiKamWa R(2024)F2Key: Dynamically Converting Your Face into a Private Key Based on COTS Headphones for Reliable Voice InteractionProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661860(127-140)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661860
Wang ZWang YYang J(2024)EarSlideProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435158:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643515
Show More Cited By

Index Terms

VoiceLive: A Phoneme Localization based Liveness Detection for Voice Authentication on Smartphones
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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cover image ACM Conferences

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

October 2016

1924 pages

ISBN:9781450341394

DOI:10.1145/2976749

General Chairs:
Edgar Weippl
SBA Research, Austria
,
Stefan Katzenbeisser
TU Darmstadt, CYSEC, Germany
,
Program Chairs:
Christopher Kruegel
University of California, Santa Barbara, USA
,
Andrew Myers
Cornell University, USA
,
Shai Halevi
IBM Research, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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New York, NY, United States

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Published: 24 October 2016

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CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security

October 24 - 28, 2016

Vienna, Austria

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CCS '16 Paper Acceptance Rate 137 of 831 submissions, 16%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

Salt Lake City , UT , USA

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Cited By

Yang YWang YChen YYe ZLi XXia ZRen YOkoshi TKo JLiKamWa R(2024)TouchTone: Smartwatch Privacy Protection via Unobtrusive Finger Touch GesturesProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661884(141-154)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661884
Duan DSun ZNi TLi SJia XXu WLi TOkoshi TKo JLiKamWa R(2024)F2Key: Dynamically Converting Your Face into a Private Key Based on COTS Headphones for Reliable Voice InteractionProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661860(127-140)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661860
Wang ZWang YYang J(2024)EarSlideProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435158:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643515
Wu CZeng Q(2024)Turning Noises to Fingerprint-Free “Credentials”: Secure and Usable Drone AuthenticationIEEE Transactions on Mobile Computing10.1109/TMC.2024.337350323:10(10161-10174)Online publication date: Oct-2024
https://doi.org/10.1109/TMC.2024.3373503
Sun XXiong JFeng CLi HWu YFang DChen X(2024)EarSSR: Silent Speech Recognition via EarphonesIEEE Transactions on Mobile Computing10.1109/TMC.2024.335671923:8(8493-8507)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2024.3356719
Chen YYu JKong LZhu YTang F(2024)Sensing Human Gait for Environment-Independent user Authentication using Commodity RFID DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.3318753(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3318753
Han FYang PDu HLi X(2024)Accuth+: Accelerometer-Based Anti-Spoofing Voice Authentication on Wrist-Worn WearablesIEEE Transactions on Mobile Computing10.1109/TMC.2023.331483723:5(5571-5588)Online publication date: May-2024
https://doi.org/10.1109/TMC.2023.3314837
Cao HLiu DJiang HLuo J(2024)MagSign: Harnessing Dynamic Magnetism for User Authentication on IoT DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2022.321685123:1(597-611)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3216851
Jeong DChoi EAhn HMartinez-Martin EPark Edel Pobil A(2024)Multi-modal Authentication Model for Occluded Faces in a Challenging EnvironmentIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33900588:5(3463-3473)Online publication date: Oct-2024
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Yang QCui KZheng Y(2024)Room-scale Voice Liveness Detection for Smart DevicesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3367269(1-14)Online publication date: 2024
https://doi.org/10.1109/TDSC.2024.3367269
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