CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Quality Assessment for Image Encryption Techniques using Fuzzy Logic System

by Haider M. Al-Mashhadi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 5
Year of Publication: 2017
Authors: Haider M. Al-Mashhadi
10.5120/ijca2017912706

Haider M. Al-Mashhadi . Quality Assessment for Image Encryption Techniques using Fuzzy Logic System. International Journal of Computer Applications. 157, 5 ( Jan 2017), 22-26. DOI=10.5120/ijca2017912706

@article{ 10.5120/ijca2017912706,
author = { Haider M. Al-Mashhadi },
title = { Quality Assessment for Image Encryption Techniques using Fuzzy Logic System },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 5 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume157/number5/26827-2017912706/ },
doi = { 10.5120/ijca2017912706 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:03:07.342388+05:30
%A Haider M. Al-Mashhadi
%T Quality Assessment for Image Encryption Techniques using Fuzzy Logic System
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 5
%P 22-26
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are many applications in image processing field; one of them is how to secure the image during transmission. In many cases there are different methods to encrypt the image. Each one of them has a different level of security that can be determined by using quality assessment techniques. The cipher image can be evaluated using various quality measuring criteria, these measures quantify certain features of the image. If there are many methods that can be applied to secure images; the question is what is the most powerful scheme that can be used among these methods? This research try to answer this question by taking three different encryption methods (RC5, Chaotic and Permutation) and measure their quality using the (PSNR, Correlation, Entropy, NPCR and UACI), the results of these criteria were input to a fuzzy logic system that was used to find the best one among them. The fuzzy logic output determine the degree of effectiveness for each method, many experiments have been executed on various images to show the ability of work to assess quality of the encryption method.

References
  1. H. R. Sheikh and A. C. Bovik, 2006. Image information and visual quality, IEEE Transaction on Image Processing 15, pp. 430–444, 2006.
  2. Z. You, A. Perkis, M. M. Hannuksela, and M. Gabbouj, 2009. Perceptual quality assessment based on visual attention analysis, in: Proceedings of ACM International Conference on Multimedia, Beijing, China, pp. 561-564, 2009.
  3. G. Zhai, W. Zhang, Y. Xu, and W. Lin, 2007. LGPS: Phase based Image Quality Assessment Metric, in: Proceedings of IEEE Workshop Signal Processing Systems, Shanghai, China, pp. 605-609, 2007.
  4. Z. Liu and R. Laganiere, 2006. On the use of phase congruency to evaluate image similarity, in: Proceedings of International Conference on Acoustics, Speech, Signal Processing, Toulouse, France, pp. 937-940, 2006.
  5. D. M. Chandler and S. S. Hemami, 2007. VSNR: A wavelet-based visual signal-to-noise ratio for natural images, IEEE Trans. Image Processing 16, pp. 2284-2298, 2007.
  6. R. Ferzli and L. J. Karam, 2007. A no-reference objective image sharpness metric based on just-noticeable blur and probability summation, in: Proceedings of International Conference on Image Processing, San Antonio, TX, pp. 445–448, 2007.
  7. F. Wei, X. Gu, and Y. Wang, 2008. Image quality assessment using edge and contrast similarity, in: Proceedings of IEEE International Joint Conference on Neural Networks, Hong Kong, China, pp. 852–855, 2008.
  8. C.-L. Yang, W.-R. Gao, and L.-M. Po, 2008. Discrete wavelet transform-based structural similarity for image quality assessment, in: Proceedings of IEEE International Conference on Image Processing, San Diego, CA, pp. 377-380, 2008.
  9. A. Shnayderman, A. Gusev, and A. M. Eskicioglu, 2006. An SVD-based grayscale image quality measure for local and global assessment, IEEE Transaction on Image Processing 15, pp. 422–429, 2006.
  10. H.-S. Han, D.-O Kim, and R.-H. Park, 2006. Structural information-based image quality assessment using LU factorization, IEEE Transaction on Consumer Electronics 55, pp. 165–171, 2006.
  11. D.-O Kim and R.-H. Park, 2006. "New image quality metric using the Harris response, IEEE Signal Processing Letters 16, pp. 616–619, 2006.
  12. D.-O Kim and R.-H. Park, 2007. "Joint feature-based visual quality assessment, Electronics Letters 43, pp. 1134-1135, 2007.
  13. L. Cui and A. R. Allen, 2008. An image quality metric based on corner, edge and symmetry maps, in: Proceedings of British Machine Vision Conference, Leeds, UK, 2008.
  14. G.-H. Chen, C.-L. Yang, and S.-L. Xie, 2006. Gradient-based structural similarity for image quality assessment, in: Proceedings of International Conference on Image Processing, Atlanta, GA, pp. 2929–2932, 2006.
  15. R. Rivest. “The RC5 Encryption Algorithm,” In: Proceedings of the Leuven Workshop on Fast Software Encryption, pp. 86–96, Springer Verlag, 1995.
  16. G.A.sathishkumar, K.Bhoopathy, N.Siriaam, "Image Encryption Based on Diffusion and Multiple Chaotic Maps", International Journal of Network Security & Its Applications, Vol: 3, No: 2, pp: 181-194, 2011.
  17. Sesha P. Indrakanti, P.S.Avadhani, "Permutation based Image Encryption Technique", International Journal of Computer Applications. Vol: 28, No: 8, pp: 45-47, 2011.
  18. Z. Wang, A.C. Bovik, H.D. Sheikh, E.P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, Vol. 13, pp. 600–612, 2004.
  19. Xuehu Yan, et al, ” A New Assessment Measure of Shadow Image Quality Based on Error Diffusion Techniques,” Journal of Information Hiding and Multimedia Signal Processing, Volume 4, Number 2, April 2013.
  20. C.E. Shannon, “Communication Theory of Secrecy Systems,” Bell System Technical Journal, vol. 28, No. 4, pp. 656-715, October 1949.
  21. G. Chen, Y. Mao, and C. Chui, "A Symmetric Image Encryption Scheme Based on 3D Chaotic Cat Maps," Chaos, Solitons and Fractals, vol. 21, pp. 749-761, 2004.
  22. Y. Mao, G. Chen, and S. Lian, "A Novel Fast Image Encryption Scheme Based on 3D Chaotic Baker Maps," Int. J. Bifurcation and Chaos in June, 2003.
  23. H.T. Sencar, M. Ramkumar, A.N. Akansu, "Data Hiding Fundamentals and Applications," New York, Elsevier Academic Press, 2004.
  24. C.C. Chang, C.C. Lin, Y.H. Chen, “Reversible Data-Embedding Scheme using Differences Between Original and Predicted Pixel Values,” IET Information Security, Vol. 2, pp. 35–46, 2008.
  25. A.N. Netravali, B.G. Haskell, "Digital Pictures: Representation, Compression and Standards”, New York, Plenum Press, 1995.
  26. M. Rabbani, P.W. Jones, "Digital Image Compression Techniques," Washington, SPIE Optical Engineering Press, 1991.
  27. L. Zadeh. "Fuzzy sets," Information Control, pp. 338-353, 1965.
  28. Dirankov, D., H. Hellendron, and M. Reinfrank. "An Introduction to Fuzzy Control," Springer New York, 1993.‏
  29. Mitaim, Sanya, and Bart Kosko. "The shape of fuzzy sets in adaptive function approximation." Fuzzy Systems, IEEE Transactions on 9.4 (2001): 637-656.‏
  30. K. M. Passino, S. Yurkovich, "Fuzzy Control," Adison Wesley Longman Inc., 1998.
  31. M. Schmidt, T. Stidsen, "Hyprid System: Genetic Algorithms, Neural Networks, and Fuzzy Logic," Denmark, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Correlation encryption entropy fuzzy logic NPCR PSNR quality assessment UACI.

Powered by PhDFocusTM