We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

The Effect of Variance Difference of Dyadic Quantized Histograms on Universal Steganalysis

by Dariush Alimoradi, Maryam Hasanzadeh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 8
Year of Publication: 2013
Authors: Dariush Alimoradi, Maryam Hasanzadeh
10.5120/10100-4746

Dariush Alimoradi, Maryam Hasanzadeh . The Effect of Variance Difference of Dyadic Quantized Histograms on Universal Steganalysis. International Journal of Computer Applications. 62, 8 ( January 2013), 19-24. DOI=10.5120/10100-4746

@article{ 10.5120/10100-4746,
author = { Dariush Alimoradi, Maryam Hasanzadeh },
title = { The Effect of Variance Difference of Dyadic Quantized Histograms on Universal Steganalysis },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 8 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number8/10100-4746/ },
doi = { 10.5120/10100-4746 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:14.619404+05:30
%A Dariush Alimoradi
%A Maryam Hasanzadeh
%T The Effect of Variance Difference of Dyadic Quantized Histograms on Universal Steganalysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 8
%P 19-24
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Steganalysis is the art and science of detecting messages hidden using steganography. The goal of steganalysis is to identify suspected packages, determine whether or not they have a payload encoded into them, and, if possible, recover that payload. Selecting a proper cover image plays a prominent role in steganography success. Various measures have been introduced to choose a proper image so far. In this work we are going to present a new measure independent of hidden message and it is just build on the image content. It is also quite effective on steganalysis and steganography success. This measure has been constructed by using histogram as the main component of image processing and it is called Variance Difference of dyadic Quantized Histograms. A quantized histogram to N is an image histogram with decreased color to N. Comparing several quantized histogram pairs by their variance demonstrates that the more the variance differences in quantized histogram pairs of an image is, the more probable the universal steganalysis failure is . Generally, universal steganalysis has less accuracy and more expected failure in detecting a true stego image. This paper considered quantized histograms to 64, 128, and 256 in grayscale JPEG images and it outlined that the effect of quantized histograms to 128, 256 is more than the other pairs.

References
  1. Sallee P. Model-based Steganography. 2003. IWDW, 154-167.
  2. P. Sallee "Model-Based Methods For Steganography And Steganalysis", International Journal of Image and Graphics, vol. 5, 2005, 167-190.
  3. Fridrich J. , Pevny T. , Kodovsky J. 2007 Statistically undetectable jpeg steganography: dead ends challenges, and opportunities. 9th workshop on Multimedia &security, Dallas, Texas, USA.
  4. Solanki K. , Sarkar A. , and Manjunath B. S. 2007. YASS: Yet Another Steganographic Scheme that Resists Blind Steganalysis. Information Hidding, 9th International Workshop, Saint Malo,France.
  5. Sarkar A. , Solanki K. , and Manjunath B. S. 2008. Further study on YASS: steganography based on randomized embedding to resist blind steganalysis. San Jose, CA, USA.
  6. Fridrich J. , Goljan M. , and Soukal D. 2004. Perturbed quantization steganography with wet paper codes. Multimedia and security, Magdeburg, Germany.
  7. Pevny T. , and Fridrich J. 2007. Merging Markov and DCT features for multi-class JPEG steganalysis. San Jose, CA, USA.
  8. Kodovsky J. , Fridrich J. 2009. Calibration revisited. 11th ACM workshop on Multimedia and security, Princeton, New Jersey, USA.
  9. Chen C. , Shi Y. Q. , Chen W. , Xuan G. 2006. Statistical Moments Based Universal Steganalysis using JPEG 2-D Array and 2-D Characteristic Function. Image Processing, IEEE International Conference Atlanta, GA.
  10. M. Saad, "Content Based Image Retrieval Literature Survey," EE 381K: Multi Dimensional Digital Signal Processing, 2008.
  11. X. Y. Luo, D. S. Wang, P. Wang, and F. L. Liu, "Review: A review on blind detection for image steganography," Signal Proces. , vol. 88, 2008,2138-2157.
  12. Kharrazi M. , Sencar H. T. , and Memon N. 2006. Cover Selection for Steganographic Embedding. IEEE International Conference on Image Processing.
  13. H. Sajedi, M. Jamzad, "BSS: Boosted steganography scheme with cover image preprocessing" Expert Systems with Applications, vol. 37, 2010, 7703-7710.
  14. H. Sajedi, M. Jamzad, "Using contourlet transform and cover selection for secure steganography," Int. J. Inf. Secur, vol. 9, 2010, 337-352.
  15. D. S. Guru, Y. H. Sharath, and S. Manjunath, "Texture Features and KNN in Classification of Flower Images," IJCA,Special Issue on RTIPPR(1), 2010,21-29.
  16. P. Bas and T. Furon. BOWS2 [Online]. Available: http://bows2. ec-lille. fr/BOWS2OrigEp3. tgz
Index Terms

Computer Science
Information Sciences

Keywords

Quantized Histogram Variance Difference Image Content

Powered by PhDFocusTM