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LIPAuth: Hand-dependent Light Intensity Patterns for Resilient User Authentication

Published: 08 May 2023 Publication History

Abstract

Authentication mechanisms deployed on access control systems undertake the responsibility of judging user identity to prevent unauthorized individuals from illegally approaching. In this article, we propose LIPAuth leveraging hand-dependent Light Intensity Pattern to Authenticate users. To be specific, lights released by a screen, are blocked and reflected by one hand above it; in this propagation process, hands exhibit user-specific ability in driving light absorption and attenuation due to owning unique structures, thereby outputting discriminative intensity patterns representing user identity. To implement LIPAuth, we first study the impact of screen contents on light intensity patterns, also explore the possibility of embedding hand structure biometrics into these patterns. We then design a customized dynamic stimulus-response mechanism for LIPAuth and make it resilient to the risks of potential registration profile leakage. Subsequently, we construct a joint pipeline consisting of signal processing and a learning-based generative adversarial network to overcome interference from variable user behaviors. More importantly, LIPAuth just utilizes common sensors to capture light signals, hence achieving low cost. We finally conduct extensive experiments in three scenarios to evaluate the authentication performance of LIPAuth prototype.

References

[1]
A. L. Jacobson. 2001. Auto-threshold peak detection in physiological signals. In IEEE EMBS, Vol. 3. 2194–2195.
[2]
Mamoun Alazab, Shamsul Huda, Jemal Abawajy, Rafiqul Islam, John Yearwood, Sitalakshmi Venkatraman, Roderic Broadhurst, et al. 2014. A hybrid wrapper-filter approach for malware detection. Academy Publisher.
[3]
Mamoun Alazab, Robert Layton, Roderic Broadhurst, and Brigitte Bouhours. 2013. Malicious spam emails developments and authorship attribution. In CTC Workshop. 58–68.
[4]
Apple. 2022. About Face ID advanced technology. Retrieved from https://support.apple.com/en-us/HT208108.
[6]
Angela I. Barbero, Eirik Rosnes, Guang Yang, and Oyvind Ytrehus. 2014. Near-field passive RFID communication: Channel model and code design. IEEE Trans. Commun. 62, 5 (2014), 1716–1727.
[7]
Bergstra James and Bengio Yoshua. 2012. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 2 (2012).
[8]
Donald J. Berndt and James Clifford. 1994. Using dynamic time warping to find patterns in time series. In KDD Workshop, Vol. 10, 359–370.
[9]
Hangcheng Cao, Daibo Liu, Hongbo Jiang, Chao Cai, Tianyue Zheng, John C. S. Lui, and Jun Luo. 2022. HandKey: Knocking-triggered robust vibration signature for keyless unlocking. IEEE Trans. Mob. Comput. (2022), 1–15. DOI:
[10]
Cao Hangcheng, Jiang Hongbo, Liu Daibo, and Xiong Jie. 2021. Evidence in hand: Passive vibration response-based continuous user authentication. In IEEE ICDCS. 1020–1030.
[11]
Cha Seunghun, Kwag Sungsu, Kim Hyoungshick, et al. 2017. Boosting the guessing attack performance on Android lock patterns with smudge attacks. In ACM ASIACCS. 313–326.
[12]
Chen Wenqiang, Chen Lin, Huang Yandao, et al. 2019. Taprint: Secure text input for commodity smart wristbands. In ACM MobiCom. 1–16.
[13]
Cheung Tsz-Him and Yeung Dit-Yan. Modals: Modality-agnostic automated data augmentation in the latent space. In ICLR.
[14]
Cisco Systems Inc. 2020. Fingerprint cloning: Myth or reality?Retrieved from https://blog.talosintelligence.com/2020/04/fngerprint-research.html.
[15]
J. B. Dawson, D. J. Barker, D. J. Ellis, et al. 1980. A theoretical and experimental study of light absorption and scattering by in vivo skin. Phys. Med. Biol. 25, 4 (1980), 695.
[16]
Dong Xibin, Yu Zhiwen, Cao Wenming, et al. 2020. A survey on ensemble learning. Front. Comput. Sci. 14, 2 (2020), 241–258.
[17]
Nesli Erdogmus and Sebastien Marcel. 2014. Spoofing face recognition with 3D masks. IEEE Trans. Inf. Forens. Secur. 9, 7 (2014), 1084–1097.
[18]
Fatemeh Fahimi, Strahinja Dosen, Kai Keng Ang, Natalie Mrachacz-Kersting, and Cuntai Guan. 2020. Generative adversarial networks-based data augmentation for brain-computer interface. IEEE Trans. Neural Netw. Learn. Syst. 32, 9 (2020), 4039–4051.
[19]
Germain Forestier, François Petitjean, Hoang Anh Dau, Geoffrey I. Webb, and Eamonn Keogh. 2017. Generating synthetic time series to augment sparse datasets. In IEEE ICDM. 865–870.
[20]
Gu Quanquan, Li Zhenhui, and Han Jiawei. 2014. Generalized Fisher score for feature selection. ArXiv Preprint.
[21]
Guo Gongde, Wang Hui, Bell David, et al. 2003. KNN model-based approach in classification. 986–996.
[22]
Hu Hu, Tian Tan, and Yanmin Qian. 2018. Generative adversarial networks based data augmentation for noise robust speech recognition. In IEEE ICASSP. 5044–5048.
[23]
Junduan Huang, Mo Tu, Weili Yang, and Wenxiong Kang. 2021. Joint attention network for finger vein authentication. IEEE Trans. Instrum. Measur. 70 (2021), 1–11.
[24]
Huang Yongzhi, Chen Kaixin, et al. 2021. Lili: Liquor quality monitoring based on light signals. In ACM MobiCom. 256–268.
[25]
Huijie Chen, Fan Li, Wan Du, Song Yang, et al. 2020. Listen to your fingers: User authentication based on geometry biometrics of touch gesture. ACM Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 4, 3 (2020), 75:1–75:23.
[26]
Impinj. 2021. Impinj Speedway RAIN RFID Readers for Flexible Solution Development. Retrieved from https://www.impinj.com/products/readers/impinj-speedway.
[27]
Jiang Hongbo, Cao Hangcheng, Liu Daibo, Xiong Jie, and Cao Zhichao. 2020. SmileAuth: Using dental edge biometrics for user authentication on smartphones. ACM Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 4, 3 (2020), 1–24.
[28]
Joseph M. Kahn and John R. Barry. 1997. Wireless infrared communications. Proc. IEEE 85, 2 (1997), 265–298.
[29]
Khamis Mohamed, Trotter Ludwig, et al. 2018. CueAuth: Comparing touch, mid-air gestures, and gaze for cue-based authentication on situated displays. ACM Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 2, 4 (2018), 1–22.
[30]
Hao Kong, Li Lu, Jiadi Yu, Yingying Chen, and Feilong Tang. 2020. Continuous authentication through finger gesture interaction for smart homes using WiFi. IEEE Trans. Mob. Comput. 20, 11 (2020), 3148–3162.
[31]
Kung Sun Yuan, Mak Man-Wai, Lin Shang-Hung, et al. 2005. Biometric Authentication: A Machine Learning Approach. Prentice Hall Professional Technical Reference New York.
[32]
Carlos Lago, Rafael Romón, Iker Pastor López, Borja Sanz Urquijo, Alberto Tellaeche, and Pablo García Bringas. 2021. Deep learning applications on cybersecurity. In HHAI. 611–621.
[33]
Sunwoo Lee, Wonsuk Choi, and Dong Hoon Lee. 2021. Usable user authentication on a smartwatch using vibration. In ACM SIGSAC CCS. 304–319.
[34]
Huining Li, Chenhan Xu, Aditya Singh Rathore, Zhengxiong Li, Hanbin Zhang, Chen Song, Kun Wang, Lu Su, Feng Lin, and Kui Ren. 2020. VocalPrint: Exploring a resilient and secure voice authentication via mmWave biometric interrogation. In ACM SenSys. 312–325.
[35]
Li Jingjie, Fawaz Kassem and Kim Younghyun. 2019. Velody: Nonlinear vibration challenge-response for resilient user authentication. In ACM CCS. 1201–1213.
[36]
Liao Zimo, Luo Zhicheng, Huang Qianyi, et al. 2021. SMART: Screen-based gesture recognition on commodity mobile devices. In ACM MobiCom. 283–295.
[37]
Chen Yingying Liu Jian, Wang Chen, and Saxena Nitesh. 2017. VibWrite: Towards finger-input authentication on ubiquitous surfaces via physical vibration. In ACM CCS. 73–87.
[38]
Liu Jian, Shi Cong, Chen Yingying, et al. 2019. CardioCam: Leveraging camera on mobile devices to verify users while their heart is pumping. In ACM MobiSys. 249–261.
[39]
Liu Jianwei, Zou Xiang, Lin Feng, et al. 2021. Hand-key: Leveraging multiple hand biometrics for attack-resilient user authentication using COTS RFID. In IEEE ICDCS. 1042–1052.
[40]
Long Huang and Chen Wang. 2022. PCR-Auth: Solving authentication puzzle challenge with encoded palm contact response. In IEEE S&P. 103–120.
[41]
Li Lu, Jiadi Yu, Yingying Chen, and Yan Wang. 2020. VocalLock: Sensing vocal tract for passphrase-independent user authentication leveraging acoustic signals on smartphones. ACM Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 4, 2 (2020), 1–24.
[42]
Ma Dong, Lan Guohao, Hassan Mahbub, et al. 2019. SolarGest: Ubiquitous and battery-free gesture recognition using solar cells. In ACM MobiCom. 1–15.
[43]
Mary W. Marzke and Robert F. Marzke. 2000. Evolution of the human hand: Approaches to acquiring, analysing and interpreting the anatomical evidence. J. Anat. 197, 1 (2000), 121–140.
[44]
Matyas Vaclav and Riha Zdenek. 2003. Toward reliable user authentication through biometrics. IEEE Secur. Priv. 1, 3 (2003), 45–49.
[45]
René Mayrhofer and Stephan Sigg. 2021. Adversary models for mobile device authentication. ACM Comput. Surv. 54, 9 (2021), 1–35.
[46]
Meng Zhen, Fu Song, Yan Jie, et al. 2020. Gait recognition for co-existing multiple people using millimeter wave sensing. In AAAI, Vol. 34. 849–856.
[47]
Mouser. 2022. Ambient Light Sensor.Retrieved from https://www.mouser.com/catalog/specsheets/temt6000.pdf.
[48]
National Aeronautics and Space Administration, USA. 2020. ANTHROPOMETRY AND BIOMECHANICS. Retrieved from https://msis.jsc.nasa.gov/sections/section03.htm,2020.
[49]
Pan Quan, Zhang Lei, Dai Guanzhong, and Zhang Hongai. 1999. Two denoising methods by wavelet transform. IEEE Transactions on Signal Processing 47, 12 (1999), 3401–3406.
[50]
Derek A. Pisner and David M. Schnyer. 2020. Support vector machine. In ML. 101–121.
[51]
Perera Pramuditha and Patel Vishal M. 2018. Face-based multiple user active authentication on mobile devices. IEEE Trans. Inf. Forens. Secur. 14, 5 (2018), 1240–1250.
[52]
Qualcomm. 2021. Qualcomm Fingerprint Sensor. Retrieved from https://www.qualcomm.com/products/government/fingerprint-sensors.
[53]
Aditya Singh Rathore, Weijin Zhu, Afee Daiyan, Chenhan Xu, Kun Wang, Feng Lin, Kui Ren, and Wenyao Xu. 2020. SonicPrint: A generally adoptable and secure fingerprint biometrics in smart devices. In ACM SenSys. 121–134.
[54]
Rathore Aditya Singh, Zhu Weijin, Daiyan Afee, et al. 2020. SonicPrint: A generally adoptable and secure fingerprint biometrics in smart devices. In ACM MobiSys. 121–134.
[55]
Roth Volker, Richter Kai, and Freidinger Rene. A PIN-entry method resilient against shoulder surfing. In ACM CCS. 236–245.
[56]
Sheng Tan, Jie Yang, and Yingying Chen. 2020. Enabling fine-grained finger gesture recognition on commodity WiFi devices. IEEE Trans. Mob. Comput. 21, 8 (2020), 2789–2802.
[57]
Tian Zhao, Wei Yu-Lin, Chang Wei-Nin, et al. 2018. Augmenting indoor inertial tracking with polarized light. In ACM MobiCom. 362–375.
[58]
University of Michigan. 2020. A Power Monitor for Android-based Mobile Platforms. http://ziyang.eecs.umich.edu/projects/powertutor/.
[59]
Raghav H. Venkatnarayan and Muhammad Shahzad. 2018. Gesture recognition using ambient light. ACM Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 2, 1 (2018), 1–28.
[60]
ntertechnology. 2021. Ambient Light Sensor. Retrieved from https://www.vishay.com/docs/81579/temt6000.pdf.
[61]
[62]
Wang Dong, Lu Huchuan, and Bo Chunjuan. 2014. Visual tracking via weighted local cosine similarity. IEEE Trans. Cyber. 45, 9 (2014), 1838–1850.
[63]
Wang Wei, Yang Lin, and Zhang Qian. 2016. Touch-and-guard: Secure pairing through hand resonance. In ACM Ubicomp. 670–681.
[64]
Wikipedia. 2022. Magnetic stripe card. Retrieved from https://en.wikipedia.org/wiki/Magnetic_stripe_card.
[65]
Yan Jeff, Blackwell Alan, Anderson Ross, and Grant Alasdair. 2004. Password memorability and security: Empirical results. IEEE Secur. Priv. 2, 5 (2004), 25–31.
[66]
Freund Yoav and Mason Llew. 1999. The alternating decision tree learning algorithm. In ICML, Vol. 99. 124–133.
[67]
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. NeurIPS 32 (2019).
[68]
Yu Nanfang, Genevet, Patrice, Kats Mikhail A., et al. 2011. Light propagation with phase discontinuities: Generalized laws of reflection and refraction. Science 334, 6054 (2011), 333–337.
[69]
Yunze Zeng, Parth H. Pathak, and Prasant Mohapatra. 2016. WiWho: WiFi-based person identification in smart spaces. In ACM/IEEE IPSN. 1–12.
[70]
Yumeng Zhang, Gaoguo Jia, Li Chen, Mingrui Zhang, and Junhai Yong. 2020. Self-paced video data augmentation by generative adversarial networks with insufficient samples. In ACM MM. 1652–1660.
[71]
Zhang David, Liu Feng, Zhao Qijun, et al. 2010. Selecting a reference high resolution for fingerprint recognition using minutiae and pores. IEEE Trans. Instrum. Measur. 60, 3 (2010), 863–871.
[72]
Zhang Xinchen, Yin Yafeng, Xie Lei, et al. 2020. TouchID: User authentication on mobile devices via inertial-touch gesture analysis. ACM Proc. ACM Interact. Mob. Wear. Ubiq. Technol. 4, 4 (2020), 1–29.
[73]
Zhao Cui, Li Zhenjiang, Liu Ting, et al. 2019. RF-Mehndi: A fingertip profiled RF identifier. In IEEE INFOCOM. 1513–1521.
[74]
Zou Qin, Wang Yanling, Wang Qian, et al. 2020. Deep learning-based gait recognition using smartphones in the wild. IEEE Trans. Inf. Forens. Secur. 15 (2020), 3197–3212.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 19, Issue 3
August 2023
597 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3584865
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 08 May 2023
Online AM: 15 December 2022
Accepted: 14 November 2022
Revised: 14 October 2022
Received: 25 June 2022
Published in TOSN Volume 19, Issue 3

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Author Tags

  1. User authentication
  2. hand structure
  3. Light Intensity Pattern
  4. resilient mechanism

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  • Research-article

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  • National Natural Science Foundation of China
  • China Scholarship Council (CSC)

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