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HandiText: Handwriting Recognition Based on Dynamic Characteristics with Incremental LSTM

Authors:

Zehong CaoAuthors Info & Claims

ACM Transactions on Data Science, Volume 1, Issue 4

Article No.: 25, Pages 1 - 18

https://doi.org/10.1145/3385189

Published: 25 November 2020 Publication History

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Abstract

The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user’s serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.

References

[1]

almosthuman2017. 2018. AI Era No Absolute Security: Baidu Mystery Lab Minutes to Crack Iris Recognition and Vein Recognition Hardware. Retrieved from https://36kr.com/p/5116996.

[2]

Amusi. 2019. Biometric Authentication Under Threat: Liveness Detection Hacking. Retrieved from https://cloud.tencent.com/developer/article/1484902/.

[3]

D. Bertolini, L. Oliveira, E. Justino, and R. Sabourin. 2008. Ensemble of classifiers for off-line signature verification. In Proceedings of the IEEE International Conference on Systems.

[4]

Zhen Li Danyan Han, Jingwen Wang and Hao Li. 2007. Comparative studies on handwriting features of Chinese and English scripts. Crim. Techn. 4 (2007), 16--18.

[5]

Anupam Das, Joseph Bonneau, Matthew Caesar, Nikita Borisov, and XiaoFeng Wang. 2014. The tangled web of password reuse. In Proceedings of the Network and Distributed System Security Conference (NDSS’14), Vol. 14. 23--26.

[6]

Li Deng. 2012. The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Sign. Process. Mag. 29, 6 (2012), 141--142.

[7]

S. Dreiseitl and L. Ohnomachado. 2002. Logistic regression and artificial neural network classification models: A methodology review.J. Biomed. Inf. 35, 5 (2002), 352--359.

Digital Library

[8]

Nitin Garg, Raghav Kukreja, and Pitambar Sharma. 2013. Revisiting defences against large scale online password guessing attacks. Int. J. Sci. Res. Publ. 3, 4 (2013).

[9]

Felix Gers and Douglas Eck. 2001. Applying LSTM to time series predictable through time-window approaches. In Proceedings of the International Conference on Artificial Neural Networks.

Digital Library

[10]

Golnaz Ghiasi and Reza Safabakhsh. 2013. Offline text-independent writer identification using codebook and efficient code extraction methods. Image Vis. Comput. 31, 5 (2013), 379--391.

Digital Library

[11]

Youn Hee Gil, Yongwha Chung, Dosung Ahn, Jihyun Moon, and Hakil Kim. 2001. Performance analysis of smart card-based fingerprint recognition for secure user authentication. In Proceedings of the IFIP Conference on Towards the E-society: E-commerce.

[12]

Grant Ho, Derek Leung, Pratyush Mishra, Ashkan Hosseini, Dawn Song, and David Wagner. 2016. Smart locks: Lessons for securing commodity internet of things devices. In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. ACM, 461--472.

Digital Library

[13]

Hennebert Jean Ingold Rolf Humm, Andreas. 2008. Combined handwriting and speech modalities for user authentication. IEEE Trans. Syst. 39, 1 (2008), 25--35.

Digital Library

[14]

IT168. 2018. iPhone Has Taken the Fight to 3D Printing by Easily Cracking Android’s Face Recognition Feature. Retrieved from https://baijiahao.baidu.com/s?id=16202437945059912238wfr=spider8for=pc.

[15]

Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Jathushan Rajasegaran, Suranga Seneviratne, and Ranga Rodrigo. 2019. TextCaps: Handwritten character recognition with very small datasets. In Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV’19).

[16]

Xiaoguang Jia. 2006. A comparative study on the identification of chinese and english signatures. J. Chin. People’s Publ. Secur. Univ. Sci. Technol. 12, 3 (2006).

[17]

Taekyoung Kwon and Sarang Na. 2014. TinyLock: Affordable defense against smudge attacks on smartphone pattern lock systems. Comput. Secur. 42, 4 (2014), 137--150.

[18]

Taekyoung Kwon, Sooyeon Shin, and Sarang Na. 2017. Covert attentional shoulder surfing: Human adversaries are more powerful than expected. IEEE Trans. Syst. Man Cybernet. Syst. 44, 6 (2017), 716--727.

[19]

Cheng Lin Liu, Fei Yin, Da Han Wang, and Qiu Feng Wang. 2011. CASIA online and offline chinese handwriting databases. In 2011 International Conference on Document Analysis and Recognition, Vol. 1. 37--41.

Digital Library

[20]

Johnny Long. 2011. No Tech Hacking: A Guide to Social Engineering, Dumpster Diving, and Shoulder Surfing. Syngress.

[21]

U. V. Marti and H. Bunke. 2002. The IAM-database: An english sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5, 1 (2002), 39--46.

[22]

Kenrick Mock, Bogdan Hoanca, Justin Weaver, and Mikal Milton. 2012. Real-time continuous iris recognition for authentication using an eye tracker. In Proceedings of the ACM Conference on Computer 8 Communications Security.

[23]

Hyeonjoon Moon. 2004. Biometrics person authentication using projection-based face recognition system in verification scenario. In Proceedings of the 1st International Conference on Biometric Authentication (ICBA’04).

[24]

Gang Pan, Zhaohui Wu, and Lin Sun. 2008. Liveness detection for face recognition. In Recent Advances in Face Recognition. IntechOpen.

[25]

P. J. Phillips, K. W. Bowyer, and P. J. Flynn. 2007. Comments on the CASIA version 1.0 iris data set. IEEE Trans. Pattern Anal. Mach. Intell. 29, 10 (2007), 1869.

Digital Library

[26]

Alain Rakotomamonjy. 2003. Variable selection using svm based criteria. J. Mach. Learn. Res. 3, 7--8 (2003), 1357--1370.

[27]

Runye. 2018. Fingerprint Unlock Can Also Be Broken. Retrieved from http://baijiahao.baidu.com/s?id=16017807066433201158wfr=spider8for=pc.

[28]

Stefan Schneegass, Frank Steimle, Andreas Bulling, Florian Alt, and Albrecht Schmidt. 2014. Smudgesafe: Geometric image transformations for smudge-resistant user authentication. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 775--786.

Digital Library

[29]

Sandeep K. Sood, Anil K. Sarje, and Kuldip Singh. 2009. Cryptanalysis of password authentication schemes: Current status and key issues. In Proceedings of the 2009 Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS’09). IEEE, 1--7.

[30]

Sandeep K. Sood, Anil K. Sarje, and Kuldip Singh. 2010. Cryptanalysis of password authentication schemes: Current status and key issues. In Proceeding of the International Conference on Methods 8 Models in Computer Science.

[31]

JC Torres. 2019. Android Q Gets “3D Touch” Pressure-Sensitivity Support. Retrieved from https://www.slashgear.com/android-q-gets-3d-touch-pressure-sensitivity-support-08572355/.

[32]

Liam Tung. 2017. IoT Devices Will Outnumber the World’s Population This Year for the First Time. Retrieved from https://www.zdnet.com/article/iot-devices-will-outnumber-the-worlds-population-this-year-for-the-first-time/.

[33]

Ding Wang and Ping Wang. 2015. Offline dictionary attack on password authentication schemes using smart cards. In Information Security. Springer, 221--237.

[34]

Rick Wash, Emilee Rader, Ruthie Berman, and Zac Wellmer. 2016. Understanding password choices: How frequently entered passwords are re-used across websites. In Proceedings of the 12th Symposium on Usable Privacy and Security (SOUPS’16). 175--188.

[35]

G. O. Williams. 2002. Iris recognition technology. IEEE Aerosp. Electr. Syst. Mag. 12, 4 (2002), 23--29.

[36]

Ying Xin. 2018. Research and implementation of handwriting recognition system based on kNN. Electronic Design Engineering 26, 7 (2018), 27--30.

[37]

Y. Song, Z. Cai, and Z. Zhang. 2017. Multi-touch authentication using hand geometry and behavioral information. In 2017 IEEE Symposium on Security and Privacy (SP'17), San Jose, CA, 2017. 357--372.

Cited By

Li WGleerup TTan JWang Y(2024)A Security Enhanced Android Unlock Scheme Based on Pinch-to-Zoom for Smart DevicesIEEE Transactions on Consumer Electronics10.1109/TCE.2023.328006470:1(3985-3993)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3280064
Tuskó GMeng WLampe B(2023)Look Closer to Touch Behavior-enabled Android Pattern Locks: A Study in the Wild2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00163(1196-1205)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00163
Zhou LWang KLai JZhang D(2023)A Comparison of a Touch-Gesture- and a Keystroke-Based Password Method: Toward Shoulder-Surfing Resistant Mobile User AuthenticationIEEE Transactions on Human-Machine Systems10.1109/THMS.2023.323632853:2(303-314)Online publication date: Apr-2023
https://doi.org/10.1109/THMS.2023.3236328
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Index Terms

HandiText: Handwriting Recognition Based on Dynamic Characteristics with Incremental LSTM
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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Information & Contributors

Information

Published In

cover image ACM/IMS Transactions on Data Science

ACM/IMS Transactions on Data Science Volume 1, Issue 4

Special Issue on Retrieving and Learning from IoT Data and Regular Papers

November 2020

148 pages

ISSN:2691-1922

DOI:10.1145/3439709

Editor:
Beng Chin Ooi
National University of Singapore, Singapore

Issue’s Table of Contents

Copyright © 2020 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 November 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 August 2019

Published in TDS Volume 1, Issue 4

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Funding Sources

National Cryptography Development Fund
Fundamental Research Funds for the Central Universities
Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars
Alibaba-ZJU Joint Research Institute of Frontier Technologies
National Natural Science Foundation of China
Provincial Key Research and Development Program of Zhejiang
Natural Science Foundation of Jiangsu Province
Ant Financial Research Funding

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Cited By

Li WGleerup TTan JWang Y(2024)A Security Enhanced Android Unlock Scheme Based on Pinch-to-Zoom for Smart DevicesIEEE Transactions on Consumer Electronics10.1109/TCE.2023.328006470:1(3985-3993)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3280064
Tuskó GMeng WLampe B(2023)Look Closer to Touch Behavior-enabled Android Pattern Locks: A Study in the Wild2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00163(1196-1205)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00163
Zhou LWang KLai JZhang D(2023)A Comparison of a Touch-Gesture- and a Keystroke-Based Password Method: Toward Shoulder-Surfing Resistant Mobile User AuthenticationIEEE Transactions on Human-Machine Systems10.1109/THMS.2023.323632853:2(303-314)Online publication date: Apr-2023
https://doi.org/10.1109/THMS.2023.3236328
Xiong YDai YMeng D(2023)Deep Frame-Point Sequence Consistent Network for Handwriting Trajectory Recovery2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00291(2151-2158)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00291
Wang KZhou LZhang D(2023)Biometrics-Based Mobile User Authentication for the Elderly: Accessibility, Performance, and Method DesignInternational Journal of Human–Computer Interaction10.1080/10447318.2022.215490340:9(2153-2167)Online publication date: Jan-2023
https://doi.org/10.1080/10447318.2022.2154903
Li WTan JZhu N(2023)Design of double-cross-based smartphone unlock mechanismComputers and Security10.1016/j.cose.2023.103204129:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103204
Li WWang YTan JZhu N(2022)DCUS: Evaluating Double-Click-Based Unlocking Scheme on SmartphonesMobile Networks and Applications10.1007/s11036-021-01842-127:1(382-391)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11036-021-01842-1
Tan MShen HXi KChai B(2022)Trajectory prediction of flying vehicles based on deep learning methodsApplied Intelligence10.1007/s10489-022-04098-853:11(13621-13642)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1007/s10489-022-04098-8
Shahid ATalukder S(2021)Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy2021 Innovations in Intelligent Systems and Applications Conference (ASYU)10.1109/ASYU52992.2021.9598983(1-6)Online publication date: 6-Oct-2021
https://doi.org/10.1109/ASYU52992.2021.9598983
Xu ZLuo MKumar NVijayakumar PLi L(2020)Privacy-Protection Scheme Based on Sanitizable Signature for Smart Mobile Medical ScenariosWireless Communications & Mobile Computing10.1155/2020/88774052020Online publication date: 24-Nov-2020
https://dl.acm.org/doi/10.1155/2020/8877405

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