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

Secured Cryptography cum Steganography Model with Large Message Embedding behind Colored Image by using Genetic Algorithm and OPA Process

by Abhishek Tripathy, Dinesh Kumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 12
Year of Publication: 2014
Authors: Abhishek Tripathy, Dinesh Kumar
10.5120/14896-3371

Abhishek Tripathy, Dinesh Kumar . Secured Cryptography cum Steganography Model with Large Message Embedding behind Colored Image by using Genetic Algorithm and OPA Process. International Journal of Computer Applications. 85, 12 ( January 2014), 43-49. DOI=10.5120/14896-3371

@article{ 10.5120/14896-3371,
author = { Abhishek Tripathy, Dinesh Kumar },
title = { Secured Cryptography cum Steganography Model with Large Message Embedding behind Colored Image by using Genetic Algorithm and OPA Process },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 12 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number12/14896-3371/ },
doi = { 10.5120/14896-3371 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:50.220601+05:30
%A Abhishek Tripathy
%A Dinesh Kumar
%T Secured Cryptography cum Steganography Model with Large Message Embedding behind Colored Image by using Genetic Algorithm and OPA Process
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 12
%P 43-49
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduced a combined cryptography and steganography model to increase the security and the embedding capability of the standard colored images. Cryptography is used in this technique to provide more security. For this purpose we used a secret key (same for sender and receiver) for encryption and decryption process. Only sender and receiver can encrypt and decrypt the whole message by this secret key. For steganography we use genetic algorithm, optimal pixel adjustment process, integer to integer wavelet transform in frequency domain, inverse integer to integer wavelet transform. Genetic algorithm is applied here to achieve a mapping function of 8*8 matrix with minimum error difference between the input and the final image. In GA we use the block based mapping method because by using it we can preserve the input image properties. After it we employed the optimal pixel adjustment process for increasing the hiding capacity of the proposed algorithm in comparison to other existing algorithms. OPA process adjusts all the pixels of image optimally. MATLAB simulation results present that the hiding capacity and imperceptibility of image increase simultaneously. By using the optimization technique such as GA we can choose the best block size to reduce the computation cost and also increase the peak signal to noise ratio.

References
  1. C. Cachin, "An Information-Theoretic Model for Steganography", in proceeding 2nd Information Hiding Workshop, vol. 1525, pp. 306-318, 1998.
  2. R. Chandramouli, N. Memon, "Analysis of LSB Based Image Steganography Techniques", IEEE pp. 1019-1022, 2001.
  3. N. F. Johnson, S. Jajodia, "Staganalysis: The Investigation of Hiding Information", IEEE, pp. 113-116, 1998.
  4. D. Artz, "Digital Steganography: Hiding Data within Data", IEEE Internet Computing, pp. 75-80, May-Jun 2001.
  5. C. Darwin, The Origin of the Species, Cambridge, Ma. , Harvard University Press, 1967.
  6. R. A. Fisher, The Genetical Theory of Natural Selection. Clarendon press, Oxford 1930.
  7. A. D. Channon, and R. I. Damper, "Towards the Evolutionary Emergence of Increasingly Complex Advantageous Behaviours". International Journal of Systems Science, 31(7), pp. 843-860, 2000.
  8. Carlos D. Toledo, "Genetic Algorithms for the numerical solutions of variational problems without analytic trial functions", arXiv:Physics/0506188, pp. 1-3, June 2005.
  9. J. Holland, "Genetic Algorithms" Sci. Am. pp. 114-116, 1992.
  10. T. Bäck and H. P. Schwefel, "An Overview Of Evolutionary Algorithms" Evolutionary Comput. 1: pp. 1-23, 1993.
  11. Allen B. Tucker (Jr. ), The Computer Science and Engineering Handbook, CRC Press, USA, pp. 557-571, 1997.
  12. J. H. Holland, Adaptive in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975.
  13. D. E. Goldberg, Genetic Algorithms, in Search, Optimization & Machine Learning. Addison Wesley, 1997.
  14. T. Bäck, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, N. Y. ,1996.
Index Terms

Computer Science
Information Sciences

Keywords

Cryptography Steganography Genetic algorithm Mapping function Optimal Pixel Adjustment process Peak signal to noise ratio MATLAB

Powered by PhDFocusTM