Robust Image Encryption Scheme for Biometric Finger Print Images using Bit Planes, DWT & Cubic Map

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.946
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Special Issue 5, November 2022

##plugins.themes.academic_pro.article.main##

Gouri Morankar
Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India

Abstract

UIDAI employs biometric finger print system to generate omnipresent identity card for Indian citizens. Biometric finger print images are therefore accepted by all Banks and Government offices to established identity of the person. Furthermore lots of private organizations employ biometric finger print system to record attendance and personal information of its employees. Consequently these biometric finger print images are stored and transmitted widely enhancing risk of misusing user personal information and cybercrimes. In this paper, robust image encryption scheme for biometric finger print images is proposed through bit planes; cubic map and DWT. The complete encryption or decryption process can be decomposed into three methods. Firstly each 8-bit biometric image is separated into 8 bit planes corresponding to each bit. Secondly DWT is applied that further encrypts the image in frequency domain by manipulating LL subbands through cubic map. Finally inverse DWT (IDWT) converts each image into spatial domain which is synthesis to from a single 8-bit encrypted image. The robustness of the proposed encryption scheme is measured through entropy, correlation coefficient, number of pixel change rate (NPCR) and unified average change intensity (UACI). Furthermore the proposed encryption scheme is also tested successfully for compression and noise attacks.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biography

Gouri Morankar, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India

Faculty, Department of Electronics & Computer Engineering,

How to Cite
Gouri Morankar. (2022). Robust Image Encryption Scheme for Biometric Finger Print Images using Bit Planes, DWT & Cubic Map. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.946
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Chatterjee, S., Roy, S., Das, A. K., Chattopadhyay, S., Kumar, N., and Vasilakos,
  2. A. V. 2018. Secure biometric-based authentication scheme using chebyshev chaotic map
  3. for multi-server environment. IEEE Transactions on Dependable and Secure Computing Vol.15, No.5, pp. 824 – 839.
  4. Li, C., Xie, T., Liu, Q., and Chen, G. 2014. Cryptanalyzing image encryption using chaotic DOI: https://doi.org/10.1007/s11071-014-1533-8
  5. logistic map. Nonlinear Dynamics Vol.78, No.2, pp. 1545 – 1551.
  6. Murugan, B., Gounden, A., and Gounder, N. 2016. Image encryption scheme based on
  7. blockbased confusion and multiple levels of diffusion. IET Computer Vision Vol.10, No.6,
  8. pp. 593 – 602.
  9. Nagar, A., Nandakumar, K., and Jain, A. K. 2012. Multibiometric cryptosystems based on DOI: https://doi.org/10.1109/TIFS.2011.2166545
  10. feature-level fusion. IEEE Transactions on Information Forensics and Security Vol.7, No.1,
  11. pp. 255 – 268.
  12. Nematzadeh, H., Enayatifar, R., Motameni, H., Guimaraes, F. G. ˜ , and Coelho, V. N.
  13. Medical image encryption using a hybrid model of modified genetic algorithm and
  14. coupled map lattices. Optical Lasers Engineering Vol.110, pp. 24 – 32.
  15. Norouzi, B., Seyedzadeh, S. M., Mirzakuchaki, S., and Mosavi, M. R. 2015. A novel
  16. image encryption based on row-column, masking and main diffusion processes with hyper
  17. chaos. Springer Multimedia Tools and Applications Vol.74, No.3, pp. 781 – 811.
  18. Shankar, K. and Lakshmanaprabu, S. K. 2018. Optimal key based homomorphic encryption
  19. for color image security aid of ant lion optimization algorithm. International Journal ofEngineering and Technology Vol.7, No.9, pp. 22 – 27.
  20. Shaque, A. and Shahid, J. 2018. Novel image encryption cryptosystem based on binary bit
  21. planes extraction and multiple chaotic maps. European Physical Journal Plus Vol.133, No.8,
  22. pp. 331.
  23. Toughi, S., Fathi, M. H., and Sekhavat, Y. A. 2017. An image encryption scheme based on
  24. elliptic curve pseudo random and advanced encryption system. Signal Processing Vol.141,
  25. pp. 217 – 227.
  26. Wen, H. and Yu, S. 2019. Cryptanalysis of an image encryption cryptosystem based on
  27. binary bit planes extraction and multiple chaotic maps. European Physical Journal
  28. Plus Vol.134, No.7, pp. 337.
  29. Zhang, Y., Zhang, L. Y., Zhou, J., Liu, L., Chen, F., and He, X. 2016. A review of
  30. compressive sensing in information security field. IEEE Access Vol.4, pp. 2507 – 2519. DOI: https://doi.org/10.1109/ACCESS.2016.2569421