CoreTemp: Coreset Sampled Templates for Multimodal Mobile Biometrics
Source abstract
Abstract
Smart devices have become the core ingredient in maintaining human society, where their applications span basic telecommunication, entertainment, education, and even critical security tasks. However, smartphone security measures have not kept pace with their ubiquitousness and convenience, exposing users to potential security breaches. Shading light on shortcomings of traditional security measures such as PINs gives rise to biometrics-based security measures. Open-set authentication with pretrained Transformers especially shows competitive performance in this context. Bringing this closer to practice, we propose CoreTemp, a greedy coreset sampled template, which offers substantially faster authentication speeds. In parallel with CoreTemp, we design a fast match algorithm where the combination shows robust performance in open-set mobile biometrics authentication. Designed to resemble the effects of ensembles with marginal increment in computation, we propose PIEformer+, where its application with CoreTemp has state-of-the-art performance. Benefiting from much more efficient authentication speeds to the best of our knowledge, we are the first to attempt identification in this context. Our proposed methodology achieves state-of-the-art results on HMOG and BBMAS datasets, particularly with much lower computational costs. In summary, this research introduces a novel integration of greedy coreset sampling with an advanced form of pretrained, implicitly ensembled Transformers (PIEformer+), greatly enhancing the speed and efficiency of mobile biometrics authentication, and also enabling identification, which sets a new benchmark in the relevant field.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: User Authentication and Security Systems · Biometric Identification and Security · Face recognition and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
AiV Research, Seoul 06745, Republic of Korea
Funding
National Research Foundation of Korea
References (45)
Show source-provided references
1. A novel authentication scheme for fingerprint based on transformer
2. Wang, X., Cheng, H., Zhang, X., and Wang, Z. (2021, January 20–25). Vision transformers for face recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.
3. Transformer-based iris recognition
4. Ktrans: A transformer-based keystroke dynamics authentication system
5. Attacks on state-of-the-art face recognition using attentional adversarial attack generative network10.1007/s11042-020-09604-z ↗
6. Jose, C.J., and Rajasree, M. (2021, January 28–29). Deep learning-based implicit continuous authentication of smartphone user. Proceedings of the Third International Conference on Communication, Computing and Electronics Systems: ICCCES 2021, Coimbatore, India.
7. Using recurrent neural networks for continuous authentication through gait analysis10.1016/j.patrec.2021.03.010 ↗
8. Mekruksavanich, S., and Jitpattanakul, A. (2021). Deep learning approaches for continuous authentication based on activity patterns using mobile sensing. Sensors, 21.10.3390/s21227519 ↗
9. Wagata, K., and Teoh, A.B.J. (2022). Few-shot continuous authentication for mobile-based biometrics. Appl. Sci., 12.10.20944/preprints202209.0210.v1 ↗
10. Hu, H., Li, Y., Zhu, Z., and Zhou, G. (2018, January 11–14). CNNAuth: Continuous authentication via two-stream convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Networking, Architecture and Storage (NAS), Chongqing, China.10.1109/NAS.2018.8515693 ↗
11. SCANet: Sensor-based continuous authentication with two-stream convolutional neural networks10.1145/3397179 ↗
12. DeFFusion: CNN-based continuous authentication using deep feature fusion
13. Deng, S., Luo, J., and Li, Y. (2021, January 19–21). Cnn-based continuous authentication on smartphones with auto augmentation search. Proceedings of the Information and Communications Security: 23rd International Conference, ICICS 2021, Chongqing, China. Proceedings, Part I 23.
14. Benegui, C., and Ionescu, R.T. (2020, January 18–22). To augment or not to augment? Data augmentation in user identification based on motion sensors. Proceedings of the Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand. Proceedings, Part V 27.10.1007/978-3-030-63823-8_93 ↗
15. Centeno, M.P., van Moorsel, A., and Castruccio, S. (2017, January 28–30). Smartphone continuous authentication using deep learning autoencoders. Proceedings of the 2017 15th Annual Conference on Privacy, Security and Trust (PST), Calgary, AB, Canada.10.1109/PST.2017.00026 ↗
16. Yoon, J., Park, J., Wagata, K., Park, H., and Teoh, A.B.J. (2023, January 29). Pretrained Implicit-Ensemble Transformer for Open-Set Authentication on Multimodal Mobile Biometrics. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada.10.1145/3581783.3612467 ↗
17. Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., Müller, E., and Kloft, M. (2018, January 10–15). Deep one-class classification. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden.
18. Agrawal, M., Mehrotra, P., Kumar, R., and Shah, R. (2021, January 4–7). Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks. Proceedings of the 2021 IEEE International Joint Conference on Biometrics (IJCB), Shenzhen, China.10.1109/IJCB52358.2021.9484366 ↗
19. Buriro, A., Ricci, F., and Crispo, B. (2021, January 2). SwipeGAN: Swiping Data Augmentation Using Generative Adversarial Networks for Smartphone User Authentication. Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning, Abu Dhabi, United Arab Emirates.10.1145/3468218.3469039 ↗
20. DAKOTA: Sensor and touch screen-based continuous authentication on a mobile banking application10.1109/ACCESS.2021.3063424 ↗
21. Using data augmentation in continuous authentication on smartphones10.1109/JIOT.2018.2851185 ↗
22. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Curran Associates Inc.
23. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
24. Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., and Gehler, P. (2022, January 18–24). Towards total recall in industrial anomaly detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.01392 ↗
25. Sener, O., and Savarese, S. (2017). Active learning for convolutional neural networks: A core-set approach. arXiv.
26. Wolsey, L.A., and Nemhauser, G.L. (2014). Integer and Combinatorial Optimization, John Wiley & Sons.
27. An elementary proof of a theorem of Johnson and Lindenstrauss10.1002/rsa.10073 ↗
28. Zhou, Z.H. (2012). Ensemble Methods: Foundations and Algorithms, CRC Press.10.1201/b12207 ↗
29. AUToSen: Deep-learning-based implicit continuous authentication using smartphone sensors10.1109/JIOT.2020.2975779 ↗
30. BehaveSense: Continuous authentication for security-sensitive mobile apps using behavioral biometrics10.1016/j.adhoc.2018.09.015 ↗
31. Mobile match on card active authentication using touchscreen biometric10.1109/TCE.2020.3029955 ↗
32. Touch-stroke dynamics authentication using temporal regression forest10.1109/LSP.2019.2916420 ↗
33. Kernel deep regression network for touch-stroke dynamics authentication10.1109/LSP.2018.2846050 ↗
34. Buriro, A., Crispo, B., Gupta, S., and Del Frari, F. (2018, January 19–21). Dialerauth: A motion-assisted touch-based smartphone user authentication scheme. Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy, Tempe, AZ, USA.10.1145/3176258.3176318 ↗
35. Towards continuous authentication on mobile phones using deep learning models10.1016/j.procs.2019.08.027 ↗
36. Integrating handcrafted features with deep representations for smartphone authentication10.1145/3517332 ↗
37. Kim, J., and Kang, P. (2022). Draw-a-deep pattern: Drawing pattern-based smartphone user authentication based on temporal convolutional neural network. Appl. Sci., 12.10.3390/app12157590 ↗
38. Stationary mobile behavioral biometrics: A survey10.1016/j.cose.2023.103184 ↗
39. Chen, H., and Shrivastava, A. (2020). Group ensemble: Learning an ensemble of convnets in a single convnet. arXiv.
40. Cohen, N., and Hoshen, Y. (2020). Sub-image anomaly detection with deep pyramid correspondences. arXiv.
41. Sinha, S., Zhang, H., Goyal, A., Bengio, Y., Larochelle, H., and Odena, A. (2020, January 13–18). Small-gan: Speeding up gan training using core-sets. Proceedings of the International Conference on Machine Learning, PMLR, Virtual.
42. Dropout: A simple way to prevent neural networks from overfitting
43. Ghiasi, G., Lin, T.Y., and Le, Q.V. (2018). Dropblock: A regularization method for convolutional networks. Advances in Neural Information Processing Systems, Curran Associates Inc.
44. HMOG: New behavioral biometric features for continuous authentication of smartphone users
45. Belman, A.K., Wang, L., Iyengar, S.S., Sniatala, P., Wright, R., Dora, R., Baldwin, J., Jin, Z., and Phoha, V.V. (2019). SU-AIS BB-MAS (Syracuse University and Assured Information Security—Behavioral Biometrics Multi-device and multi-Activity data from Same users) Dataset. IEEE Dataport, 10.