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http://dx.doi.org/10.33851/JMIS.2022.9.2.103

Presentation Attacks in Palmprint Recognition Systems  

Sun, Yue (School of Software, Nanchang Hangkong University)
Wang, Changkun (School of Information Engineering, Nanchang Hangkong University)
Publication Information
Journal of Multimedia Information System / v.9, no.2, 2022 , pp. 103-112 More about this Journal
Abstract
Background: A presentation attack places the printed image or displayed video at the front of the sensor to deceive the biometric recognition system. Usually, presentation attackers steal a genuine user's biometric image and use it for presentation attack. In recent years, reconstruction attack and adversarial attack can generate high-quality fake images, and have high attack success rates. However, their attack rates degrade remarkably after image shooting. Methods: In order to comprehensively analyze the threat of presentation attack to palmprint recognition system, this paper makes six palmprint presentation attack datasets. The datasets were tested on texture coding-based recognition methods and deep learning-based recognition methods. Results and conclusion: The experimental results show that the presentation attack caused by the leakage of the original image has a high success rate and a great threat; while the success rates of reconstruction attack and adversarial attack decrease significantly.
Keywords
Presentation Attack; Reconstruction Attack; Adversarial Attack; Palmprint Recognition;
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1 L. Fei, J.Wen, Z. Zhang, K. Yan, and Z. Zhong, "Local multiple directional pattern of palmprint image," in International Conference on Pattern Recognition (ICPR), Cancun, pp. 3013-3018, Dec. 2016.
2 H. Xu, L. Leng, A. B. J. Teoh, and Z. Jin, "Multi-task pre-training with soft biometrics for transfer-learning palmprint recognition," Neural Processing Letters, pp. 1-18, Apr. 2022.
3 D. Zhang, W. K. Kong, J. You, and M. Wong, "Online palmprint identification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041-1050, Sep. 2003.   DOI
4 Z. Sun, T. Tan, Y. Wang, and S. Z. Li, "Ordinal palmprint represention for personal identification," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, pp. 279-284, Jun. 2005.
5 A. Kong, D. Zhang, and M. Kamel, "Palmprint identification using feature-level fusion," Pattern Recognition, vol. 39, no. 3, pp. 478-487, Aug. 2005.   DOI
6 W. Jia, D. S. Huang, and D. Zhang, "Palmprint verification based on robust line orientation code," Pattern Recognition, vol. 41, no. 5, pp. 1504-1513, Oct. 2007.   DOI
7 D. Jeong, B. G. Kim, and S. Y. Dong, "deep joint spatiotemporal network (DJSTN) for efficient facial expression recognition," Sensors, vol. 20, no. 7, p. 1936, Mar. 2020.   DOI
8 L. Leng, A. B. J Teoh, M. Li, and M. K. Khan, "A remote cancelable palmprint authentication protocol based on multi-directional two-dimensional PalmPhasor-fusion," Security and Communication Networks, vol. 7, no. 11, pp. 1860-1871, Nov. 2014.   DOI
9 L. Fei, Y. Xu, W. Tang, and D. Zhang, "Double-orientation code and nonlinear matching scheme for palmprint recognition," Pattern Recognition, vol. 49, pp. 89-101, Aug. 2015.   DOI
10 L. Leng, A. B. J. Teoh, M. Li, and M. K. Khan, "Analysis of correlation of 2DPalmHash Code and orientation range suitable for transposition," Neurocomputing, vol. 131 pp. 377-387, May 2014.   DOI
11 A. B. J. Teoh and L. Leng, "Special issue on advanced biometrics with deep learning," Applied Sciences, vol. 10, no. 13, p. 4453, Jun. 2020.   DOI
12 S. J. Park, B. G. Kim, and N. Chilamkurti, "A robust Facial expression recognition algorithm based on multi-rate feature fusion scheme," Sensors, vol. 21, no. 21, p. 6954, Oct. 2021.   DOI
13 J. H. Kim, G. S. Hong, B. G. Kim, and D. P. Dogra, "Deepgesture: Deep learning-based gesture recognition scheme using motion sensors," Displays, vol. 55, pp. 38-45, Dec. 2018.   DOI
14 L. Leng, F. Gao, Q. Chen, and C. Kim, "Palmprint recognition system on mobile devices with double-line-single-point assistance," Personal and Ubiquitous Computing, vol. 22., no. 1, pp. 93-104, Dec. 2018.   DOI
15 S. Jandial, P. Mangla, S. Varshney, and V. Balasubramanian, "Advgan++: Harnessing latent layers for adversary generation," in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, Seoul, Oct. 2019.
16 N. Carlini and D. Wagner, "Towards Evaluating the Robustness of Neural Networks," in 2017 IEEE Symposium on Security and Privacy (SP), San Jose, pp. 39-57, Jun. 2017.
17 S. M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, "Deepfool: A simple and accurate method to fool deep neural networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 2574-2582, Jun. 2016.
18 C. Xiao, B. Li, J. Y. Zhu, W. He, M. Liu, and D. Song, Generating adversarial examples with adversarial networks, https://arxiv.org/abs/1801.02610, 2018.
19 X. Bai, N. Gao, Z. Zhang, and D. Zhang, "3D palmprint identification combining blocked ST and PCA," Pattern Recognition Letters, vol. 100, no. 2017, pp. 89-95, Dec. 2017.   DOI
20 L. Leng, J. Zhang, M. K. Khan, X. Chen, and K. Alghathbar, "Dynamic weighted discrimination power analysis: A novel approach for face and palmprint recognition in DCT domain," International Journal of Physical Sciences, vol. 5, no. 17, pp. 2543-2554, Dec. 2010.
21 L. Leng, M. Li, and C. Kim, "Dual-source discrimination power analysis for multi-instance contactless palmprint recognition," Multimedia Tools and Applications, vol. 76, no. 1, pp. 333-354, Nov. 2017.   DOI
22 A. Adler, "Images can be regenerated from quantized biometric match score data," in Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513), Niagara Falls, pp. 469-472, May 2004.
23 D. Kondratyuk, L. Yuan, Y. Li, L. Zhang, M. Tan, and M. Brown, et al., "Movinets: Mobile video networks for efficient video recognition," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16020-16030, Jun. 2021.
24 L. Leng, A. B. J. Teoh, and M. Li, "Simplified 2DPalmHash code for secure palmprint verification," Multimedia Tools and Applications, vol. 76, no. 6, pp. 8373-8398, Apr. 2017.   DOI
25 L. Leng and A. B. J. Teoh, "Alignment-free row-co-occurrence cancelable palmprint fuzzy vault," Pattern Recognition, vol. 48, no. 7, pp. 2290-2303, Jul. 2015.   DOI
26 C. Kauba, S. Kirchgasser, V. Mirjalili, A. Uhl, and A. Ross, "Inverse biometrics: Generating vascular images From binary templates," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 4, pp. 464-478, Apr. 2021.   DOI
27 J. Galbally, C. McCool, J. Fierrez, S. Marcel, and J. Ortega-Garcia, "On the vulnerability of face verification systems to hill-climbing attacks," Pattern Recognition, vol. 43, no. 3, pp. 1027-1038, Mar. 2010.   DOI
28 U. Uludag and A. K. Jain, "Attacks on biometric systems:a case study in fingerprints," in Security, Steganography, and Watermarking of Multimedia Contents VI, California, pp. 622-633, Jun. 2004.
29 M. Gomez-Barrero, J. Galbally, J. Fierrez, and J. Ortega-Garcia, "Face verification put to test:a hill-climbing attack based on the uphill-simplex algorithm," in 5th IAPR International Conference on Biometrics (ICB), New Delhi, pp. 40-45, Apr. 2012.
30 C. Rathgeb and A. Uhl, "Attacking iris recognition:An efficient hill-climbing technique," in 20th International Conference on Pattern Recognition, Istanbul, pp. 1217-1220, Oct. 2010.
31 J. Galbally, A. Ross, M. Gomez-Barrero, J. Fierrez and J. Ortega-Garcia, "Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms," Computer Vision and Image Understanding, vol. 117, no. 10, pp. 1512-1525, Oct. 2013.   DOI
32 Y. Sun, L. Leng, Z. Jin, and B. G. Kim, "Reinforced palmprint reconstruction attacks in biometric systems," Sensors, vol. 22, no. 2 p. 591, Jan. 2022.   DOI
33 I. J. Goodfellow, J. Shlens, and C. Szegedy, "Explaining and harnessing adversarial examples," in International Conference on Learning Representations (ICLR), San Diego, May 2015.
34 A. Kurakin, I. Goodfellow, and S. Bengio, Adversarial Machine Learning at Scale," https://arxiv.org/abs/1611.01236, Feb. 2017.
35 Y. Liu, H. Yuan, Z. Wang, and S. Ji, "Global pixel transformers for virtual staining of microscopy images," IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 2256-2266, Jun. 2020.   DOI
36 Lu. Leng, Z. Yang, and W. Min, "Democratic voting downsampling for coding-based palmprint recognition," IET Biometrics, vol. 9, no. 6, pp. 290-296, Aug. 2020.   DOI
37 Z. Yang, L. Leng, and W. Min, "extreme downsampling and joint feature for coding-based palmprint recognition," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, Nov. 2021.
38 Z. Yang, J. Li, W. Min, and Q. Wang, "Real-time pre-identification and cascaded detection for tiny faces," Applied Sciences, vol. 9, no. 20, p. 4344, Oct. 2019.   DOI
39 L. Leng, Z. Yang, C. Kim, and Y. Zhang, "A lightweight practical framework for feces detection and trait recognition," Sensors, vol. 20, no. 9, p. 2644, May. 2020.   DOI
40 D. Zhong and J. Zhu, "Centralized large margin cosine loss for open-set deep palmprint recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 6, pp. 1559-1568, Jun. 2020.   DOI
41 W. M. Matkowski, T. Chai, and A. W. K. Kong, "Palmprint recognition in uncontrolled and uncooperative environment," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1601-1615, Oct. 2020.   DOI
42 X. Liang, J. Yang, G. Lu, and D. Zhang, "CompNet: Competitive neural network for palmprint recognition using learnable gabor kernels," IEEE Signal Processing Letters, vol. 28, pp. 1739-1743, Aug. 2021.   DOI
43 T. Wu, L. Leng, M. K. Khan ,and F. A. Khan, "Palmprint-palmvein fusion recognition based on deep hashing network," IEEE Access, vol. 9, pp. 135816-135827, Sep. 2021.   DOI
44 L. Leng and J. Zhang, "Palmhash code vs. palmphasor code," Neurocomputing, vol. 108, no. 2, pp. 1-12, May 2013.   DOI
45 C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, "Intriguing properties of neural networks," in International Conference on Learning Representations (ICLR), Banff, Apr. 2014.
46 Z. Guo, D. Zhang, L. Zhang, and W. Zuo, "Palmprint verification using binary orientation co-occurrence vector," Pattern Recognition Letters, vol. 30, no. 13, pp. 1219-1227, May.2009.   DOI
47 A. K. Kong and D. Zhang, "Competitive coding scheme for palmprint verification," in Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, pp. 520-523, Aug. 2004.
48 F. Wang, L. Leng, A. B. J. Teoh, and J. Chu, "Palmprint false acceptance attack with a generative adversarial network (GAN)," Applied Sciences, vol. 10, no. 23, p. 8547, Nov. 2020.   DOI
49 Y. Xu, L. Fei, J. Wen, and D. Zhang, "Discriminative and robust competitive code for palmprint recognition," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 2, pp. 232-241, Feb. 2018.   DOI
50 L. Leng and J. Zhang, "Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security," Journal of Network and Computer Applications, vol. 34, no. 6, pp. 1979-1989, Nov. 2011.   DOI
51 Y. Liu and A. Kumar, "Contactless palmprint identification using deeply learned residual features," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 2, pp. 172-181, Apr. 2020.   DOI