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Information Security using Visual Secret Sharing Scheme and Solution to Potential Attacks

by Jesalkumari Varolia, R.R. Sedamkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 16
Year of Publication: 2021
Authors: Jesalkumari Varolia, R.R. Sedamkar
10.5120/ijca2021921501

Jesalkumari Varolia, R.R. Sedamkar . Information Security using Visual Secret Sharing Scheme and Solution to Potential Attacks. International Journal of Computer Applications. 183, 16 ( Jul 2021), 42-48. DOI=10.5120/ijca2021921501

@article{ 10.5120/ijca2021921501,
author = { Jesalkumari Varolia, R.R. Sedamkar },
title = { Information Security using Visual Secret Sharing Scheme and Solution to Potential Attacks },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2021 },
volume = { 183 },
number = { 16 },
month = { Jul },
year = { 2021 },
issn = { 0975-8887 },
pages = { 42-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number16/32013-2021921501/ },
doi = { 10.5120/ijca2021921501 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:01.119663+05:30
%A Jesalkumari Varolia
%A R.R. Sedamkar
%T Information Security using Visual Secret Sharing Scheme and Solution to Potential Attacks
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 16
%P 42-48
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently images are used almost everywhere as an information transfer. As growing call for of protection, user authentication resides in earlier in records protection and performs an important position in shielding customers privacy. On-line transactions have become very usual place In this virtual era and numerous attacks are concerns. This paper discusses many potential attacks on visual secret sharing system and offering more safety than current method. In the proposed method, secret image is divided into many shares and distributed among the participants. The method used is recursive visual cryptography so many shares can be concealed in one share which gives better manageability. The main focus is on better image quality of reconstructed Image and protection from potential attacks on generated shares. At the decryption end super resolution is used to improve image quality and this paper achieved 92% accuracy and 95% SSIM The suggested method can be useful in many applications like online voting system, online banking and any other system where authentication is essential.

References
  1. Naor and A. Shamir, Visual cryptography, in "Advances in Cryptology { EUROCRYPT '94", A.DeSantis, ed., Lecture Notes in Computer Science 950 (1995), 1-12
  2. E. Biham and A. Itzkovitz, “Visual cryptography with polarization,” in RUMP Session of CRYPTO’98, 1997.I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
  3. College of Engineering, Aurangabad, M.S., India“Survey of Visual Cryptography Schemes” International Journal of Security and Its Applications Vol. 4, No. 2, April, 2010.
  4. Mrs.Jesalkumari Varolia , Dr.R.R.Sedamkar, Recursive Visual Cryptography for Multiple Secret Image Sharing, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-X, Issue-X
  5. A Verifiable Visual Cryptography Scheme Using Neural Networks Deng Yuqiao1,a , Song Ge2,
  6. Dian, R., Li, S., Guo, A., Fang, L.: Deep hyperspectral image sharpening. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2018)
  7. Dong, C., Deng, Y., Change Loy, C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 576– 584 (2015)
  8. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution usingdeep convolutional networks. IEEE Trans. Pattern Anal. Mach.Intell. 38(2), 295–307 (2016)
  9. Duarte, A., Codevilla, F., Gaya, J.D.O., Botelho, S.S.: A dataset to evaluate underwater image restoration methods. In: OCEANS 2016-Shanghai, pp. 1–6. IEEE (2016)
  10. Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43(12), 2959–2965 (1995)
  11. Fan, D.P., Lin, Z., Zhang, Z., Zhu, M., Cheng, M.M.: RethinkingRGB-D salient object detection: models, data sets, and large-scalebenchmarks. IEEE Trans Neural Netw Learn Syst (2020)
  12. Fu, K., Fan, D.P., Ji, G.P., Zhao, Q.: Jl-dcf: Joint learning and densely-cooperative fusion framework for rgb-d salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3052–3062 (2020)
  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for imagerecognition. In: Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition, pp. 770–778 (2016)
  14. Hou, Y.C., Quan, Z.Y., Tsai, C.F., Tseng, A.Y.: Block-based progressive visual secret sharing. Inf. Sci. 233, 290–304 (2013)
  15. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of humansegmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In:Proceedings of 8th International Conference Computer Vision,vol. 2, pp. 416–423 (2001)
  16. Mhala, N.C., Jamal, R., Pais, A.R.: Randomised visual secretsharing scheme for grey-scale and colour images. IET ImageProcessing 12, 422–431(9) (2018). http://digitalibrary.theiet.org/content/journals/10.1049/iet-ipr.2017.0759
  17. Mhala, N.C., Pais, A.R.: Contrast enhancement of progressivevisual secret sharing (PVSS) scheme for gray-scale and colorimages using super-resolution. Sig. Process. 162, 253–267 (2019)
  18. 26. Naor, M., Shamir, A.: Visual cryptography. In: Workshop on the Theory and Application of Cryptographic Techniques, pp. 1–12. Springer, Berlin (1994)
  19. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd (2006) 28. Shivani, S.: VMVC: verifiable multi-tone visual cryptography. Multimedia Tools Appl., pp. 1–20 (2017)
  20. Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1920–1927 (2013)
  21. Hou, Y.C., Quan, Z.Y.: Progressive visual cryptography with unexpanded shares. IEEE Trans. Circuits Syst. Video Technol. 21(11),1760–1764 (2011)
  22. Timofte, R., De Smet, V., Van Gool, L.: A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conference on Computer Vision, pp. 111–126. Springer, Berlin (2014)
  23. Wang, R.Z.: Region incrementing visual cryptography. IEEE Signal Process. Lett. 16(8), 659–662 (2009) 32. Wang, R.Z., Lee, Y.K., Huang, S.Y., Chia, T.L.: Multilevel visual secret sharing. In: Innovative Computing, Information and Control, 2007. ICICIC’07. Second International Conference on, pp. 283– 283. IEEE (2007)
  24. Wang, Z., Arce, G.R., Di Crescenzo, G.: Halftone visual cryptography via error diffusion. IEEE Trans. Inf. Forensics Secur. 4(3), 383–396 (2009).
  25. Mohammed Hassan, Chakravarthy Bhagvati ‘Structural Similarity Measure for Color Images’, April 2012 International Journal of Computer Applications.
Index Terms

Computer Science
Information Sciences

Keywords

Visual Secret Sharing Information Security

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