基于声感知的移动终端身份认证综述
作者:
基金项目:

国家自然科学基金(62202180, U20B2049, U21B2018, 62132011)


Survey on Acoustic-sensing-based Authentication on Mobile Devices
Author:
  • 摘要
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  • 访问统计
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  • 参考文献 [132]
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    摘要:

    随着移动终端的普及和用户隐私数据保护需求的增强, 基于移动终端的身份认证研究引起了广泛关注. 近年来, 移动终端的音频传感器为设计性能优良的新颖身份认证方案提供了更大的灵活性和可拓展性. 在调研了大量相关科研文献的基础上, 首先按照依赖凭据和感知方法的不同将基于声感知的移动终端身份认证方案进行分类, 并描述相应的攻击模型; 然后梳理移动终端基于不同认证凭据和基于声感知的身份认证国内外研究进展, 并进行分析、总结和对比; 最后结合当前研究的困难和不足, 给出衡量身份认证系统性能的两大指标(安全性和实用性), 对未来的研究方向进行展望.

    Abstract:

    With the popularity of mobile devices and the enhancement of users’ requirements for privacy protection, studies of user authentication on mobile devices have attracted widespread attention. Recently, the audio infrastructures of mobile devices have provided greater flexibility and scalability for the design of novel user authentication schemes with excellent performance. After surveying a large number of related works, this study first classifies acoustic sensing-based user authentication schemes on mobile devices according to the difference in authentication metrics and sensing methods and describes the corresponding attack model. Then, it analyzes and compares single authentication metric-based and acoustic sensing-based user authentication schemes on mobile devices. Finally, combined with the problems of existing works, this study gives two metrics (security and practicability) to measure the performance of the user authentication system and discuss future research directions.

    参考文献
    [1] Ericsson. Ericsson mobility report. 2022. https://www.ericsson.com/49d3a0/assets/local/reports-papers/mobility-report/documents/2022/ericsson-mobility-report-june-2022.pdf
    [2] Ye GX, Tang ZY, Fang DY, Chen XJ, Wolff W, Aviv AJ, Wang Z. A video-based attack for Android pattern lock. ACM Trans. on Privacy and Security, 2018, 21(4): 19.
    [3] Chen DJ, Zhao ZH, Qin X, Luo YH, Cao MS, Xu H, Liu AF. MagLeak: A learning-based side-channel attack for password recognition with multiple sensors in IIoT environment. IEEE Trans. on Industrial Informatics, 2022, 18(1): 467–476.
    [4] Yang E, Fang S, Markwood I, Liu Y, Zhao SQ, Lu Z, Zhu HJ. Wireless training-free keystroke inference attack and defense. IEEE/ACM Trans. on Networking, 2022, 30(4): 1733–1748.
    [5] Zhou M, Wang Q, Yang JX, Li Q, Jiang PP, Chen YJ, Wang ZB. Stealing your Android patterns via acoustic signals. IEEE Trans. on Mobile Computing, 2021, 20(4): 1656–1671.
    [6] Qin L, Peng F, Long M, Ramachandra R, Busch C. Vulnerabilities of unattended face verification systems to facial components-based presentation attacks: An empirical study. ACM Trans. on Privacy and Security, 2022, 25(1): 4.
    [7] Rathore AS, Shen YJ, Xu CH, Snyderman J, Han JS, Zhang F, Li ZX, Lin F, Xu WY, Ren K. FakeGuard: Exploring haptic response to mitigate the vulnerability in commercial fingerprint anti-spoofing. In: Proc. of the 29th Annual Network and Distributed System Security Symp. San Diego: The Internet Society, 2022. 1–17.
    [8] Wang C, Wang Y, Chen YY, Liu HB, Liu J. User authentication on mobile devices: Approaches, threats and trends. Computer Networks, 2020, 170: 107118.
    [9] Bai Y, Lu L, Cheng J, Liu J, Chen YY, Yu JD. Acoustic-based sensing and applications: A survey. Computer Networks, 2020, 181: 107447.
    [10] 卢立, 俞嘉地, 李明禄. 基于声波感知的移动设备实时防窃方法研究. 计算机学报, 2020, 43(10): 2002–2018.
    Lu L, Yu JD, Li ML. Towards a real-time anti-theft method for mobile devices leveraging acoustic sensing. Chinese Journal of Computers, 2020, 43(10): 2002–2018 (in Chinese with English abstract).
    [11] Bonneau J, Preibusch S, Anderson R. A birthday present every eleven wallets? The security of customer-chosen banking pins. In: Proc. of the 16th Int’l Conf. on Financial Cryptography and Data Security. Kralendijk: Springer, 2012. 25–40. [doi: 10.1007/978-3-642-32946-3_3]
    [12] Zhang Q, Wang D, Zhao R, Yu YG, Shen JJ. Sensing to hear: Speech enhancement for mobile devices using acoustic signals. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021, 5(3): 137.
    [13] Shi D, Tao D, Wang JT, Yao MY, Wang ZB, Chen HJ, Helal S. Fine-grained and context-aware behavioral biometrics for pattern lock on smartphones. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021, 5(1): 33.
    [14] Chauhan J, Hu YN, Seneviratne S, Misra A, Seneviratne A, Lee Y. BreathPrint: Breathing acoustics-based user authentication. In: Proc. of the 15th Annual Int’l Conf. on Mobile Systems, Applications, and Services. Niagara Falls: ACM, 2017. 278–291.
    [15] Zhou B, Xie ZX, Zhang YN, Lohokare J, Gao RP, Ye F. Robust human face authentication leveraging acoustic sensing on smartphones. IEEE Trans. on Mobile Computing, 2022, 21(8): 3009–3023.
    [16] Karapanos N, Marforio C, Soriente C, Capkun S. Sound-proof: Usable two-factor authentication based on ambient sound. In: Proc. of the 24th USENIX Security Symp. Washington: USENIX Association, 2015. 483–498.
    [17] Wang Q, Lin X, Zhou M, Chen YJ, Wang C, Li Q, Luo XY. VoicePop: A pop noise based anti-spoofing system for voice authentication on smartphones. In: Proc. of the 2019 IEEE Conf. on Computer Communications. Paris: IEEE, 2019. 2062–2070.
    [18] Zhang LH, Tan S, Yang J. Hearing your voice is not enough: An articulatory gesture based liveness detection for voice authentication. In: Proc. of the 2017 ACM SIGSAC Conf. on Computer and Communications Security. Dallas: ACM, 2017. 57–71.
    [19] Zheng BL, Jiang PP, Wang Q, Li Q, Shen C, Wang C, Ge YJ, Teng QY, Zhang SY. Black-box adversarial attacks on commercial speech platforms with minimal information. In: Proc. of the 2021 ACM SIGSAC Conf. on Computer and Communications Security. ACM, 2021. 86–107.
    [20] Yu JD, Lu L, Chen YY, Zhu YM, Kong LH. An indirect eavesdropping attack of keystrokes on touch screen through acoustic sensing. IEEE Trans. on Mobile Computing, 2021, 20(2): 337–351.
    [21] Zhou M, Wang Q, Yang JX, Li Q, Xiao F, Wang ZB, Chen XF. PatternListener: Cracking Android pattern lock using acoustic signals. In: Proc. of the 2018 ACM SIGSAC Conf. on Computer and Communications Security. Toronto: ACM, 2018. 1775–1787.
    [22] Shirvanian M, Vo S, Saxena N. Quantifying the breakability of voice assistants. In: Proc. of the 2019 IEEE Int’l Conf. on Pervasive Computing and Communications. Kyoto: IEEE, 2019. 1–11. [doi: 10.1109/PERCOM.2019.8767399]
    [23] Zhou M, Qin Z, Lin X, Hu SS, Wang Q, Ren K. Hidden voice commands: Attacks and defenses on the VCS of autonomous driving cars. IEEE Wireless Communications, 2019, 26(5): 128–133.
    [24] Wenger E, Bronckers M, Cianfarani C, Cryan J, Sha A, Zheng HT, Zhao BY. “Hello, it's me”: Deep learning-based speech synthesis attacks in the real world. In: Proc. of the 2021 ACM SIGSAC Conf. on Computer and Communications Security. ACM, 2021. 235–251.
    [25] Chen GK, Chenb S, Fan LL, Du XN, Zhao Z, Song F, Liu Y. Who is real bob? Adversarial attacks on speaker recognition systems. In: Proc. of the 2021 IEEE Symp. on Security and Privacy. San Francisco: IEEE, 2021. 694–711. [doi: 10.1109/SP40001.2021.00004]
    [26] Uellenbeck S, Dürmuth M, Wolf C, Holz T. Quantifying the security of graphical passwords: The case of Android unlock patterns. In: Proc. of the 2013 ACM SIGSAC Conf. on Computer & Communications Security. Berlin: ACM, 2013. 161–172.
    [27] Markert P, Bailey DV, Golla M, Dürmuth M, Aviv AJ. This PIN can be easily guessed: Analyzing the security of smartphone unlock PINs. In: Proc. of the 2020 IEEE Symp. on Security and Privacy. San Francisco: IEEE, 2020. 286–303.
    [28] 王平, 汪定, 黄欣沂. 口令安全研究进展. 计算机研究与发展, 2016, 53(10): 2172–2188.
    Wang P, Wang D, Huang XY. Advances in password security. Journal of Computer Research and Development, 2016, 53(10): 2172–2188 (in Chinese with English abstract).
    [29] 汪定, 邹云开, 陶义, 王彬. 基于循环神经网络和生成式对抗网络的口令猜测模型研究. 计算机学报, 2021, 44(8): 1519–1534.
    Wang D, Zou YK, Tao Y, Wang B. Password guessing based on recurrent neural networks and generative adversarial networks. Chinese Journal of Computers, 2021, 44(8): 1519–1534. (in Chinese with English abstract).
    [30] Lee MK. Security notions and advanced method for human shoulder-surfing resistant PIN-entry. IEEE Trans. on Information Forensics and Security, 2014, 9(4): 695–708.
    [31] Shukla D, Kumar R, Serwadda A, Phoha VV. Beware, your hands reveal your secrets! In: Proc. of the 2014 ACM SIGSAC Conf. on Computer and Communications Security. Scottsdale: ACM, 2014. 904–917. [doi: 10.1145/2660267.2660360]
    [32] Khan H, Hengartner U, Vogel D. Evaluating attack and defense strategies for smartphone PIN shoulder surfing. In: Proc. of the 2018 CHI Conf. on Human Factors in Computing Systems. Montreal: ACM, 2018. 164. [doi: 10.1145/3173574.3173738]
    [33] von Zezschwitz E, De Luca A, Brunkow B, Hussmann H. SwiPIN: Fast and secure PIN-entry on smartphones. In: Proc. of the 33rd Annual ACM Conf. on Human Factors in Computing Systems. Seoul: ACM, 2015. 1403–1406. [doi: 10.1145/2702123.2702212]
    [34] Krombholz K, Hupperich T, Holz T. Use the force: Evaluating force-sensitive authentication for mobile devices. In: Proc. of the 12th USENIX Conf. on Usable Privacy and Security. Denver: USENIX Association, 2016. 207–219. [doi: 10.5555/3235895.3235913]
    [35] Yan Q, Han J, Li YJ, Zhou JY, Deng RH. Designing leakage-resilient password entry on touchscreen mobile devices. In: Proc. of the 8th ACM SIGSAC Symp. on Information, Computer and Communications Security. Hangzhou: ACM, 2013. 37–48.
    [36] Li ZJ, Li M, Mohapatra P, Han JS, Chen SY. iType: Using eye gaze to enhance typing privacy. In: Proc. of the 2017 IEEE Conf. on Computer Communications. Atlanta: IEEE, 2017. 1–9. [doi: 10.1109/INFOCOM.2017.8057233]
    [37] Alsuhibany SA. A camouflage text-based password approach for mobile devices against shoulder-surfing attack. Security and Communication Networks, 2021, 2021: 6653076.
    [38] Mayer P, Gerber N, Reinheimer B, Rack P, Braun K, Volkamer M. I (don’t) see what you typed there! Shoulder-surfing resistant password entry on gamepads. In: Proc. of the 2019 CHI Conf. on Human Factors in Computing Systems. Glasgow: ACM, 2019. 549. [doi: 10.1145/3290605.3300779]
    [39] Castelluccia C, Dürmuth M, Perito D. Adaptive password-strength meters from Markov models. In: Proc. of the 19th Annual Network and Distributed System Security Symp. San Diego: The Internet Society, 2012.
    [40] Kelley PG, Komanduri S, Mazurek ML, Shay R, Vidas T, Bauer L, Christin N, Cranor LF, Lopez J. Guess again (and again and again): Measuring password strength by simulating password-cracking algorithms. In: Proc. of the 2012 IEEE Symp. on Security and Privacy. San Francisco: IEEE, 2012. 523–537. [doi: 10.1109/SP.2012.38]
    [41] Dhamija R, Perrig A. Déjà VU—A user study using images for authentication. In: Proc. of the 9th USENIX Security Symp. Denver: USENIX Association, 2000. 4–4.
    [42] Davis D, Monrose F, Reiter MK. On user choice in graphical password schemes. In: Proc. of the 13th USENIX Security Symp. San Diego: USENIX, 2004. 151–164.
    [43] De Angeli A, Coutts M, Coventry L, Johnson GI, Cameron D, Fischer MH. VIP: A visual approach to user authentication. In: Proc. of the 2002 Working Conf. on Advanced Visual Interfaces. Trento: ACM, 2002. 316–323. [doi: 10.1145/1556262.1556312]
    [44] Sun HP, Wang K, Li X, Qin N, Chen Z. PassApp: My APP is my password! In: Proc. of the 17th Int’l Conf. on Human-computer Interaction with Mobile Devices and Services. Copenhagen: ACM, 2015. 306–315. [doi: 10.1145/2785830.2785880]
    [45] Jermyn I, Mayer A, Monrose F, Reiter MK, Rubin AD. The design and analysis of graphical passwords. In: Proc. of the 8th Conf. on USENIX Security Symp. Washington: USENIX Association, 1999. 1. [doi: 10.5555/1251421.1251422]
    [46] Sherman M, Clark G, Yang YL, Sugrim S, Modig A, Lindqvist J, Oulasvirta A, Roos T. User-generated free-form gestures for authentication: Security and memorability. In: Proc. of the 12th Annual Int’l Conf. on Mobile Systems, Applications, and Services. Bretton Woods: ACM, 2014. 176–189. [doi: 10.1145/2594368.2594375]
    [47] Sae-Bae N, Memon N. Online signature verification on mobile devices. IEEE Trans. on Information Forensics and Security, 2014, 9(6): 933–947.
    [48] Cho G, Huh JH, Cho J, Oh S, Song Y, Kim H. SysPal: System-guided pattern locks for Android. In: Proc. of the 2017 IEEE Symp. on Security and Privacy. San Jose: IEEE, 2017. 338–356. [doi: 10.1109/SP.2017.61]
    [49] 姚沐言, 陶丹. 基于上采样单分类的智能手机手势密码隐式身份认证机制. 计算机科学, 2020, 47(11): 19–24.
    Yao MY, Tao D. Implicit authentication mechanism of pattern unlock based on over-sampling and one-class classification for smartphones. Computer Science, 2020, 47(11): 19–24 (in Chinese with English abstract).
    [50] Forman T, Aviv A. Double patterns: A usable solution to increase the security of Android unlock patterns. In: Proc. of the 36th Annual Computer Security Applications Conf. Austin: ACM, 2020. 219–233. [doi: 10.1145/3427228.3427252]
    [51] Munyendo CW, Grant M, Markert P, Forman TJ, Aviv AJ. Using a blocklist to improve the security of user selection of Android patterns. In: Proc. of the 17th USENIX Conf. on Usable Privacy and Security. 2021. 3. [doi: 10.5555/3563572.3563575]
    [52] Dirik AE, Memon N, Birget JC. Modeling user choice in the PassPoints graphical password scheme. In: Proc. of the 3rd Symp. on Usable Privacy and Security. Pittsburgh: ACM, 2007. 20–28. [doi: 10.1145/1280680.1280684]
    [53] Raghavendra R, Busch C, Yang B. Scaling-robust fingerprint verification with smartphone camera in real-life scenarios. In: Proc. of the 6th IEEE Int’l Conf. on Biometrics: Theory, Applications and Systems. Arlington: IEEE, 2013. 1–8.
    [54] Komeili M, Armanfard N, Hatzinakos D. Liveness detection and automatic template updating using fusion of ECG and fingerprint. IEEE Trans. on Information Forensics and Security, 2018, 13(7): 1810–1822.
    [55] Park E, Cui XN, Nguyen THB, Kim H. Presentation attack detection using a tiny fully convolutional network. IEEE Trans. on Information Forensics and Security, 2019, 14(11): 3016–3025.
    [56] Wu C, He K, Chen J, Zhao ZM, Du RY. Liveness is not enough: Enhancing fingerprint authentication with behavioral biometrics to defeat puppet attacks. In: Proc. of the 29th USENIX Security Symp. USENIX Association, 2020. 2219–2236.
    [57] Taigman Y, Yang M, Ranzato MA, Wolf L. DeepFace: Closing the gap to human-level performance in face verification. In: Proc. of the 2014 IEEE Conf. on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014. 1701–1708. [doi: 10.1109/CVPR.2014.220]
    [58] Aswal V, Tupe O, Shaikh S, Charniya NN. Single camera masked face identification. In: Proc. of the 19th IEEE Int’l Conf. on Machine Learning and Applications. Miami: IEEE, 2020. 57–60. [doi: 10.1109/ICMLA51294.2020.00018]
    [59] Li Y, Li YJ, Yan Q, Kong HC, Deng RH. Seeing your face is not enough: An inertial sensor-based liveness detection for face authentication. In: Proc. of the 22nd ACM SIGSAC Conf. on Computer and Communications Security. Denver: ACM, 2015. 1558–1569. [doi: 10.1145/2810103.2813612]
    [60] 田野, 项世军. 基于LBP和多层DCT的人脸活体检测算法. 计算机研究与发展, 2018, 55(3): 643–650.
    Tian Y, Xiang SJ. LBP and multilayer DCT based anti-spoofing countermeasure in face liveness detection. Journal of Computer Research and Development, 2018, 55(3): 643–650 (in Chinese with English abstract).
    [61] Liu AJ, Zhao CX, Yu ZT, Wan J, Su AY, Liu X, Tan ZC, Escalera S, Xing JL, Liang YY, Guo GD, Lei Z, Li SZ, Zhang D. Contrastive context-aware learning for 3D high-fidelity mask face presentation attack detection. IEEE Trans. on Information Forensics and Security, 2022, 17: 2497–2507.
    [62] Genovese A, Piuri V, Plataniotis KN, Scotti F. PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition. IEEE Trans. on Information Forensics and Security, 2019, 14(12): 3160–3174.
    [63] Song YP, Cai ZM, Zhang ZL. Multi-touch authentication using hand geometry and behavioral information. In: Proc. of the 2017 IEEE Symp. on Security and Privacy. San Jose: IEEE, 2017. 357–372. [doi: 10.1109/SP.2017.54]
    [64] Ma Z, Yang YL, Liu XM, Liu Y, Ma SQ, Ren K, Yao C. EmIr-Auth: Eye movement and iris-based portable remote authentication for smart grid. IEEE Trans. on Industrial Informatics, 2020, 16(10): 6597–6606.
    [65] Fahmi PA, Kodirov E, Choi DJ, Lee GS, Azli AMF, Sayeed S. Implicit authentication based on ear shape biometrics using smartphone camera during a call. In: Proc. of the 2012 IEEE Int’l Conf. on Systems, Man, and Cybernetics. Seoul: IEEE, 2012. 2272–2276. [doi: 10.1109/ICSMC.2012.6378079]
    [66] 肖剑, 李思卓, 董威, 李清华, 胡芳. 基于心电与光电容积脉搏波特征层融合的身份识别方法. 电子与信息学报, 2021, 43(10): 3010–3017.
    Xiao J, Li SZ, Dong W, Li QF Hu F. An identity recognition method based on electrocardiograph and photoplethysmograph feature fusion. Journal of Electronics & Information Technology, 2021, 43(10): 3010–3017 (in Chinese with English abstract).
    [67] Zhang CL, Koishida K, Hansen JHL. Text-independent speaker verification based on triplet convolutional neural network embeddings. IEEE/ACM Trans. on Audio, Speech, and Language Processing, 2018, 26(9): 1633–1644.
    [68] Wan L, Wang Q, Papir A, Moreno IL. Generalized end-to-end loss for speaker verification. In: Proc. of the 2018 IEEE Int’l Conf. on Acoustics, Speech and Signal Processing. Calgary: IEEE, 2018. 4879–4883. [doi: 10.1109/ICASSP.2018.8462665]
    [69] 余玲飞, 刘强. 基于深度循环网络的声纹识别方法研究及应用. 计算机应用研究, 2019, 36(1): 153–158.
    Yu LF, Liu Q. Research and application of deep recurrent neural networks based voiceprint recognition. Application Research of Computers, 2019, 36(1): 153–158 (in Chinese with English abstract).
    [70] Li HN, Xu CH, Rathore AS, Li ZX, Zhang HB, Song C, Wang K, Su L, Lin F, Ren K, Xu WY. VocalPrint: Exploring a resilient and secure voice authentication via mmWave biometric interrogation. In: Proc. of the 18th Conf. on Embedded Networked Sensor Systems. ACM, 2020. 312–325.
    [71] Sprager S, Juric MB. An efficient HOS-based gait authentication of accelerometer data. IEEE Trans. on Information Forensics and Security, 2015, 10(7): 1486–1498.
    [72] Zou Q, Wang YL, Wang Q, Zhao Y, Li QQ. Deep learning-based gait recognition using smartphones in the wild. IEEE Trans. on Information Forensics and Security, 2020, 15: 3197–3212.
    [73] 施沫寒, 王志海. 一种基于时间序列特征的可解释步态识别方法. 中国科学: 信息科学, 2020, 50(3): 438–460.
    Shi MH, Wang ZH. An interpretable gait recognition method based on time series features. Scientia Sinica Informationis, 2020, 50(3): 438–460 (in Chinese with English abstract).
    [74] van Hamme T, Rúa EA, Preuveneers D, Joosen W. On the security of biometrics and fuzzy commitment cryptosystems: A study on gait authentication. IEEE Trans. on Information Forensics and Security, 2021, 16: 5211–5224.
    [75] Bo C, Zhang L, Li XY, Huang QY, Wang Y. SilentSense: Silent user identification via touch and movement behavioral biometrics. In: Proc. of the 19th Annual Int’l Conf. on Mobile Computing & Networking. Miami: ACM, 2013. 187–190.
    [76] Sae-Bae N, Ahmed K, Isbister K, Memon N. Biometric-rich gestures: A novel approach to authentication on multi-touch devices. In: Proc. of the 2012 SIGCHI Conf. on Human Factors in Computing Systems. Austin: ACM, 2012. 977–986.
    [77] Shahzad M, Zhang SH. Augmenting user identification with WiFi based gesture recognition. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 134.
    [78] Shi C, Liu J, Liu HB, Chen YY. Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT. In: Proc. of the 18th ACM Int’l Symp. on Mobile Ad Hoc Networking and Computing. Chennai: ACM, 2017. 5. [doi: 10.1145/3084041.3084061]
    [79] Chauhan J, Rajasegaran J, Seneviratne S, Misra A, Seneviratne A, Lee Y. Performance characterization of deep learning models for breathing-based authentication on resource-constrained devices. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(4): 158.
    [80] Zhou QQ, Yang YN, Hong F, Feng Y, Guo ZW. User identification and authentication using keystroke dynamics with acoustic signal. In: Proc. of the 12th Int’l Conf. on Mobile Ad-hoc and Sensor Networks. Hefei: IEEE, 2016. 445–449.
    [81] Rathore AS, Zhu WJ, Daiyan A, Xu CH, Wang K, Lin F, Ren K, Xu WY. SonicPrint: A generally adoptable and secure fingerprint biometrics in smart devices. In: Proc. of the 18th Int’l Conf. on Mobile Systems, Applications, and Services. Toronto: ACM, 2020. 121–134. [doi: 10.1145/3386901.3388939]
    [82] Zou YP, Lei HB, Wu KS. Beyond legitimacy, also with identity: Your smart earphones know who you are quietly. IEEE Trans. on Mobile Computing, 2023, 22(6): 3179–3192.
    [83] Gao Y, Jin YC, Chauhan J, Choi S, Li JY, Jin ZP. Voice in ear: Spoofing-resistant and passphrase-independent body sound authentication. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021, 5(1): 12.
    [84] Zhou Z, Diao WR, Liu XY, Zhang KH. Acoustic fingerprinting revisited: Generate stable device ID stealthily with inaudible sound. In: Proc. of the 2014 ACM SIGSAC Conf. on Computer and Communications Security. Scottsdale: ACM, 2014. 429–440.
    [85] Das A, Borisov N, Caesar M. Do you hear what I hear? Fingerprinting smart devices through embedded acoustic components. In: Proc. of the 2014 ACM SIGSAC Conf. on Computer and Communications Security. Scottsdale: ACM, 2014. 441–452.
    [86] Luo D, Korus P, Huang JW. Band energy difference for source attribution in audio forensics. IEEE Trans. on Information Forensics and Security, 2018, 13(9): 2179–2189.
    [87] Tan JY, Wang XL, Nguyen CT, Shi Y. SilentKey: A new authentication framework through ultrasonic-based lip reading. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(1): 36.
    [88] Lu L, Yu JD, Chen YY, Liu HB, Zhu YM, Liu YF, Li ML. LipPass: Lip reading-based user authentication on smartphones leveraging acoustic signals. In: Proc. of the 2018 IEEE Conf. on Computer Communications. Honolulu: IEEE, 2018. 1466–1474.
    [89] Lu L, Yu JD, Chen YY, Liu HB, Zhu YM, Kong LH, Li ML. Lip reading-based user authentication through acoustic sensing on smartphones. IEEE/ACM Trans. on Networking, 2019, 27(1): 447–460.
    [90] Wang YX, Chen YN, Bhuiyan ZA, Han Y, Zhao SH, Li JX. Gait-based human identification using acoustic sensor and deep neural network. Future Generation Computer Systems, 2018, 86: 1228–1237.
    [91] Ding F, Wang D, Zhang Q, Zhao R. ASSV: Handwritten signature verification using acoustic signals. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3(3): 80.
    [92] Zhao R, Wang D, Zhang Q, Jin XY, Liu K. Smartphone-based handwritten signature verification using acoustic signals. Proc. of the ACM on Human-computer Interaction, 2021, 5(ISS): 499.
    [93] Gao Y, Wang W, Phoha VV, Sun W, Jin ZP. EarEcho: Using ear canal echo for wearable authentication. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3(3): 81.
    [94] Lu L, Yu JD, Chen YY, Wang Y. VocalLock: Sensing vocal tract for passphrase-independent user authentication leveraging acoustic signals on smartphones. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 51.
    [95] Huang L, Wang C. Notification privacy protection via unobtrusive gripping hand verification using media sounds. In: Proc. of the 27th Annual Int’l Conf. on Mobile Computing and Networking. New Orleans: ACM, 2021. 491–504. [doi: 10.1145/3447993.3483277]
    [96] Shrestha B, Shirvanian M, Shrestha P, Saxena N. The sounds of the phones: Dangers of zero-effort second factor login based on ambient audio. In: Proc. of the 2016 ACM SIGSAC Conf. on Computer and Communications Security. Vienna: ACM, 2016. 908–919.
    [97] Shrestha P, Shrestha B, Saxena N. Home alone: The insider threat of unattended wearables and a defense using audio proximity. In: Proc. of the 2018 IEEE Conf. on Communications and Network Security. Beijing: IEEE, 2018. 1–9. [doi: 10.1109/CNS.2018.8433216]
    [98] Shrestha P, Saxena N. Listening watch: Wearable two-factor authentication using speech signals resilient to near-far attacks. In: Proc. of the 11th ACM Conf. on Security & Privacy in Wireless and Mobile Networks. Stockholm: ACM, 2018. 99–110.
    [99] Ren YZ, Wen P, Liu HB, Zheng ZR, Chen YY, Huang PC, Li HW. Proximity-echo: Secure two factor authentication using active sound sensing. In: Proc. of the 2021 IEEE Conf. on Computer Communications. Vancouver: IEEE, 2021. 1–10.
    [100] Zhou B, Lohokare J, Gao RP, Ye F. EchoPrint: Two-factor authentication using acoustics and vision on smartphones. In: Proc. of the 24th Annual Int’l Conf. on Mobile Computing and Networking. New Delhi: ACM, 2018. 321–336. [doi: 10.1145/3241539.3241575]
    [101] Chen HJ, Li F, Du W, Yang S, Conn M, Wang Y. Listen to your fingers: User authentication based on geometry biometrics of touch gesture. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(3): 75.
    [102] Chen YL, Ni T, Xu WT, Gu T. SwipePass: Acoustic-based second-factor user authentication for smartphones. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022, 6(3): 106.
    [103] Zhou M, Wang Q, Lin X, Zhao Y, Jiang PP, Li Q, Shen C, Wang C. PressPIN: Enabling secure PIN authentication on mobile devices via structure-borne sounds. IEEE Trans. on Dependable and Secure Computing, 2023, 20(2): 1228–1242.
    [104] Wu C, Chen J, He K, Zhao ZM, Du RY, Zhang C. EchoHand: High accuracy and presentation attack resistant hand authentication on commodity mobile devices. In: Proc. of the 2022 ACM SIGSAC Conf. on Computer and Communications Security. Los Angeles: ACM, 2022. 2931–2945. [doi: 10.1145/3548606.3560553]
    [105] Han DQ, Chen YM, Li T, Zhang R, Zhang YC, Hedgpeth T. Proximity-proof: Secure and usable mobile two-factor authentication. In: Proc. of the 24th Annual Int’l Conf. on Mobile Computing and Networking. New Delhi: ACM, 2018. 401–415.
    [106] Wu LB, Yang JX, Zhou M, Chen YJ, Wang Q. LVID: A multimodal biometrics authentication system on smartphones. IEEE Trans. on Information Forensics and Security, 2020, 15: 1572–1585.
    [107] Yang YL, Wang Y, Chen YY, Wang C. EchoLock: Towards low-effort mobile user identification leveraging structure-borne echos. In: Proc. of the 15th ACM Asia Conf. on Computer and Communications Security. Taipei: ACM, 2020. 772–783.
    [108] Zhang SH, Das A. HandLock: Enabling 2-FA for smart home voice assistants using inaudible acoustic signal. In: Proc. of the 24th Int’l Symp. on Research in Attacks, Intrusions and Defenses. San Sebastian: ACM, 2021. 251–265. [doi: 10.1145/3471621.3471866]
    [109] Liu D, Wang Q, Zhou M, Jiang PP, Li Q, Shen C, Wang C. SoundID: Securing mobile two-factor authentication via acoustic signals. IEEE Trans. on Dependable and Secure Computing, 2023, 20(2): 1687–1701.
    [110] Wang ZF, Wei G, He QH. Channel pattern noise based playback attack detection algorithm for speaker recognition. In: Proc. of the 2011 Int’l Conf. on Machine Learning and Cybernetics. Guilin: IEEE, 2011. 1708–1713. [doi: 10.1109/ICMLC.2011.6016982]
    [111] Zhang LH, Tan S, Yang J, Chen YY. Voicelive: A phoneme localization based liveness detection for voice authentication on smartphones. In: Proc. of the 2016 ACM SIGSAC Conf. on Computer and Communications Security. Vienna: ACM, 2016. 1080–1091. [doi: 10.1145/2976749.2978296]
    [112] Shang JC, Chen S, Wu J. Defending against voice spoofing: A robust software-based liveness detection system. In: Proc. of the 15th IEEE Int’l Conf. on Mobile Ad Hoc and Sensor Systems. Chengdu: IEEE, 2018. 28–36. [doi: 10.1109/MASS.2018.00016]
    [113] Jiang PP, Wang Q, Lin X, Zhou M, Ding WB, Wang C, Shen C, Li Q. Securing liveness detection for voice authentication via pop noises. IEEE Trans. on Dependable and Secure Computing, 2023, 20(2): 1702–1718.
    [114] Wang Y, Cai WD, Gu T, Shao W, Li YN, Yu Y. Secure your voice: An oral airflow-based continuous liveness detection for voice assistants. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 3(4): 157.
    [115] Yan C, Long Y, Ji XY, Xu WY. The catcher in the field: A fieldprint based spoofing detection for text-independent speaker verification. In: Proc. of the 2019 ACM SIGSAC Conf. on Computer and Communications Security. London: ACM, 2019. 1215–1229.
    [116] Ahmed ME, Kwak IY, Huh JH, Kim I, Oh T, Kim H. VOID: A fast and light voice liveness detection system. In: Proc. of the 29th USENIX Security Symp. USENIX Association, 2020. 2685–2702.
    [117] Li ZH, Shi C, Zhang TF, Xie Y, Liu J, Yuan B, Chen YY. Robust detection of machine-induced audio attacks in intelligent audio systems with microphone array. In: Proc. of the 2021 ACM SIGSAC Conf. on Computer and Communications Security. ACM, 2021. 1884–1899.
    [118] Meng Y, Li JC, Pillari M, Deopujari A, Brennan L, Shamsie H, Zhu HJ, Tian Y. Your microphone array retains your identity: A robust voice liveness detection system for smart speakers. In: Proc. of the 31st USENIX Security Symp. Boston: USENIX Association, 2022. 1077–1094.
    [119] Zhang LH, Tan S, Chen YY, Yang J. A continuous articulatory-gesture-based liveness detection for voice authentication on smart devices. IEEE Internet of Things Journal, 2022, 9(23): 23320–23331.
    [120] Zhang LH, Tan S, Wang Z, Ren YL, Wang Z, Yang J. VibLive: A continuous liveness detection for secure voice user interface in IoT environment. In: Proc. of the 36th Annual Computer Security Applications Conf. Austin: ACM, 2020. 884–896.
    [121] Chen HX, Wang W, Zhang J, Zhang Q. EchoFace: Acoustic sensor-based media attack detection for face authentication. IEEE Internet of Things Journal, 2019, 7(3): 2152–2159.
    [122] Zhou M, Wang Q, Li Q, Zhou WY, Yang JX, Shen C. Securing face liveness detection on mobile devices using unforgeable lip motion patterns. IEEE Trans. on Mobile Computing, 2024, 23(10): 9772–9788.
    [123] Kong CQ, Zheng KX, Wang SQ, Rocha A, Li HL. Beyond the pixel world: A novel acoustic-based face anti-spoofing system for smartphones. IEEE Trans. on Information Forensics and Security, 2022, 17: 3238–3253.
    [124] Levalle Y. Bypassing biometric systems with 3D printing. 2020. https://www.youtube.com/watch?v=hJ35ApLKpN4
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周满,李向前,王骞,李琦,沈超,周雨庭.基于声感知的移动终端身份认证综述.软件学报,2025,36(5):2229-2253

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  • 收稿日期:2022-12-26
  • 最后修改日期:2023-07-17
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