Skip to main content

Advertisement

Springer Nature Link
Log in

Two novel style-transfer palmprint reconstruction attacks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Palmprint has been widely used for personal authentication in many applications, such that the assessment of recognition system security is important. Online attacks of palmprint recognition are much more difficult than offline attacks due to the fewer permissible login and authentication attempts, the unusability of the matching scores, and less training data. A cross-database attack is another challenging problem, where the images reconstructed from a template can still be effective in attacking the systems with other templates. To achieve online cross-database attacks and ensure that the reconstructed images are high-quality, two novel style-transfer methods are proposed to attack coding-based palmprint recognition systems. The two methods are both based on a convolutional neural network, but their optimization objects are different. In the first method, the optimization object is the input image, where a high-quality image can be reconstructed from the binary template. In the second method, the style-transfer neural network is trained with a template dataset and only one style image to reduce the style loss between the source and target domains. The trained style-transfer network can reconstruct approximately 270 images per second. The two methods have highly impressive attack success rates and satisfactorily meet the requirements of the evaluation system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Shao H, Zhong D, Du X (2021) Deep distillation hashing for unconstrained palmprint recognition. IEEE Trans Instrum Meas 70:1–13

    Google Scholar 

  2. Dwivedi R, Dey S (2019) A tokenless cancellable scheme for multimodal biometric systems. Appl Intell 49:1016–1035

    Article  Google Scholar 

  3. Asthana R, Walia G S, Gupta A (2021) Random area-perimeter method for generation of unimodal and multimodal cancelable biometric templates. Appl Intell 51:7281–7297

    Article  Google Scholar 

  4. Galbally J, McCool C, Fierrez J, Marcel S, Ortega-Garcia J (2010) On the vulnerability of face verification systems to hill-climbing attacks. Pattern Recogn 43(3):1027–1038

    Article  MATH  Google Scholar 

  5. Dong X, Jin Z, Jin A T B (2019) A genetic algorithm enabled similarity-based attack on cancellable biometrics. In: 2019 IEEE 10th international conference on biometrics theory, applications and systems (BTAS). IEEE, pp 1–8

  6. Tarek M, Hamouda E, Abohamama AS (2022) Multi-instance cancellable biometrics schemes based on generative adversarial network. Appl Intell 52:501–513

    Article  Google Scholar 

  7. Fei L, Zhang B, Zhang L, Jia W, Wen J, Wu J (2020) Learning compact multifeature codes for palmprint recognition from a single training image per palm. IEEE Trans Multimed 23:2930–2942

    Article  Google Scholar 

  8. Fei L, Zhang B, Tian C, Teng S, Wen J (2021) Jointly learning multi-instance hand-based biometric descriptor. Inf Sci 562:1–12

    Article  MathSciNet  Google Scholar 

  9. Wan M, Yao Y, Zhan T, Yang G (2021) Supervised low-rank embedded regression (slrer) for robust subspace learning. IEEE Transactions on Circuits and Systems for Video Technology

  10. Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Applic 76(1):333–354

    Article  Google Scholar 

  11. Fei L, Wen J, Zhang Z, Yan K, Zhong Z (2016) Local multiple directional pattern of palmprint image. In: 2016 23rd International conference on pattern recognition (ICPR). IEEE, pp 3013–3018

  12. Liu Y, Kumar A (2020) Contactless palmprint identification using deeply learned residual features. IEEE Trans Biometr Behav Identity Sci 2(2):172–181

    Article  Google Scholar 

  13. Liang X, Yang J, Lu G, Zhang D (2021) Compnet: competitive neural network for palmprint recognition using learnable Gabor kernels. IEEE Signal Process Lett 28:1739–1743

    Article  Google Scholar 

  14. Leng L, Yang Z, Min W (2020) Democratic voting downsampling for coding-based palmprint recognition. IET Biometr 9(6):290–296

    Article  Google Scholar 

  15. Yang Z, Leng L, Min W (2020) Extreme downsampling and joint feature for coding-based palmprint recognition. IEEE Trans Instrum Meas 70:1–12

    Article  Google Scholar 

  16. Fei L, Lu G, Jia W, Teng S, Zhang D (2018) Feature extraction methods for palmprint recognition: a survey and evaluation. IEEE Trans Syst Man Cybern: Syst 49(2):346–363

    Article  Google Scholar 

  17. Zhang D, Kong W-K, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050

    Article  Google Scholar 

  18. Guo Z, Zhang D, Zhang L, Zuo W (2009) Palmprint verification using binary orientation co-occurrence vector. Pattern Recogn Lett 30(13):1219–1227

    Article  Google Scholar 

  19. Sun Z, Tan T, Wang Y, Li S Z (2005) Ordinal palmprint represention for personal identification [represention read representation]. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 279–284

  20. Kong A, Zhang D, Kamel M (2006) Palmprint identification using feature-level fusion. Pattern Recogn 39(3):478–487

    Article  MATH  Google Scholar 

  21. Kong AW-K, Zhang D (2004) Competitive coding scheme for palmprint verification. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004., vol 1. IEEE, pp 520–523

  22. Jia W, Huang D-S, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recogn 41(5):1504–1513

    Article  MATH  Google Scholar 

  23. Fei L, Xu Y, Tang W, Zhang D (2016) Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recogn 49:89–101

    Article  Google Scholar 

  24. Fei L, Xu Y, Zhang D (2016) Half-orientation extraction of palmprint features. Pattern Recogn Lett 69:35–41

    Article  Google Scholar 

  25. Xu Y, Fei L, Wen J, Zhang D (2016) Discriminative and robust competitive code for palmprint recognition. IEEE Trans Syst Man Cybern: Syst 48(2):232–241

    Article  Google Scholar 

  26. Feng Y C, Lim M-H, Yuen P C (2014) Masquerade attack on transform-based binary-template protection based on perceptron learning. Pattern Recogn 47(9):3019–3033

    Article  Google Scholar 

  27. Wang F, Leng L, Teoh A B J, Chu J (2020) Palmprint false acceptance attack with a generative adversarial network (gan). Appl Sci 10(23):8547

    Article  Google Scholar 

  28. Mai G, Cao K, Yuen P C, Jain A K (2019) On the reconstruction of face images from deep face templates. IEEE Trans Pattern Anal Mach Intell 41(5):1188–1202

    Article  Google Scholar 

  29. Qin L, Peng F, Venkatesh S, Ramachandra R, Long M, Busch C (2020) Low visual distortion and robust morphing attacks based on partial face image manipulation. IEEE Trans Biometr Behav Ident Sci 3(1):72–88

    Article  Google Scholar 

  30. Wang G, Han H, Shan S, Chen X (2019) Improving cross-database face presentation attack detection via adversarial domain adaptation. In: 2019 International conference on biometrics (ICB). IEEE, pp 1–8

  31. Shin S J, You S C, Jeon H, Jung J W, An M H, Park R W, Roh J (2021) Style transfer strategy for developing a generalizable deep learning application in digital pathology. Comput Methods Prog Biomed 198:105815

    Article  Google Scholar 

  32. Gatys L A, Ecker A S, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414–2423

  33. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  34. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, pp 694–711

  35. Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: The missing ingredient for fast stylization. arXiv:1607.08022

  36. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition. Ieee, pp 248–255

  37. Zhang L, Li L, Yang A, Shen Y, Yang M (2017) Towards contactless palmprint recognition: a novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recogn 69:199–212

    Article  Google Scholar 

Download references

Acknowledgments

This work is funded in part by the National Natural Science Foundation of China by the National Natural Science Foundation of China under Grants 61866028, 61866025, and 62162045, the Technology Innovation Guidance Program Project (Special Project of Technology Cooperation, Science and Technology Department of Jiangxi Province) under Grant 20212BDH81003.

Author information

Authors and Affiliations

  1. School of Software, Nanchang Hangkong University, Nanchang, China

    Ziyuan Yang, Lu Leng & Jun Chu

  2. College of Computer Science, Sichuan University, Chengdu, China

    Ziyuan Yang

  3. PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau

    Bob Zhang

  4. School of Information Engineering, Nanchang Hangkong University, Nanchang, China

    Ming Li

Authors
  1. Ziyuan Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lu Leng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bob Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ming Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jun Chu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Ziyuan Yang: Writing-Original draft preparation; Conceptualization; Methodology; Validation; Software.

Lu Leng: Methodology, Validation; Supervision; Project administration.

Bob Zhang: Reviewing; Investigation; Formal analysis; Visualization.

Ming Li: Reviewing; Data curation; Resources.

Jun Chu: Reviewing; Editing; Resources.

Corresponding author

Correspondence to Lu Leng.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Z., Leng, L., Zhang, B. et al. Two novel style-transfer palmprint reconstruction attacks. Appl Intell 53, 6354–6371 (2023). https://doi.org/10.1007/s10489-022-03862-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03862-0

Keywords