CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

New Steganalysis Method using GLCM and Neural Network

by Sedighe Ghanbari, Manije Keshtegary, Najme Ghanbari
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 7
Year of Publication: 2012
Authors: Sedighe Ghanbari, Manije Keshtegary, Najme Ghanbari
10.5120/5709-6266

Sedighe Ghanbari, Manije Keshtegary, Najme Ghanbari . New Steganalysis Method using GLCM and Neural Network. International Journal of Computer Applications. 42, 7 ( March 2012), 46-52. DOI=10.5120/5709-6266

@article{ 10.5120/5709-6266,
author = { Sedighe Ghanbari, Manije Keshtegary, Najme Ghanbari },
title = { New Steganalysis Method using GLCM and Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 7 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number7/5709-6266/ },
doi = { 10.5120/5709-6266 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:45.121561+05:30
%A Sedighe Ghanbari
%A Manije Keshtegary
%A Najme Ghanbari
%T New Steganalysis Method using GLCM and Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 7
%P 46-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Steganography is the art of hidden writing and secret communication. The goal of Steganography is to hide a message in a multimedia objet such as image. Steganalysis is the art and science of detecting such the hidden messages. The Gray level Co-occurrence matrix (GLCM) is the matrix containing information about the relationship between values of adjacent pixel in an image. In this paper, we extract features from GLCM that are different between cover image (image without hidden information) and stego image (image with hidden information). In the proposed algorithm, first, we use a combined method of steganography based on both location and conversion to hide the information in the original image and call it image-steg1 image. Then, we hide the information in imagesteg1 again and call it image-steg2. Using GLCM matrix properties, we investigate some different features in the GLCM of the original image and stego images. We can extract features that are different between these images. Features are used for training neural network and the classification step was accomplished using four layers Multi Layer Perceptron (MLP) neural network. We tested our algorithm on 800 standard image databases and we detected 80% of stego images. Therefore, our proposed algorithm efficiency is 80%.

References
  1. Dumitrescu S. , Wu X. and Wang X. , "Detection of LSB steganography via sample pair analysis", IEEE Transactions on Signal Processing, Vol. 51, No. 7, pp. 1995-2007, 2003.
  2. Shieh C. -S, Huang H. -C, Wang F. -H and Pan J. -S, "Genetic Watermarking Based On Transform-Domain Techniques", Pattern Recognition, Vol. 37, pp: 555-565, 2004.
  3. Hopper N. , "Toward a theory of steganography", Ph. D. Thesis, School of Computer Science, Carnegie Mellon University, July 2004.
  4. Swanson M. , Kobayashi M. , and Tewfik A. , "Multimedia data embedding and watermarking technologies", Proceedings of the IEEE, Vol. 86, No. 6, pp. 1064-1087, 1998.
  5. Cox I. , Kilian J. , Leighton T. and Shamoon T. , "Secure spread spectrum watermarking for multimedia", IEEE Transactions on Image Processing, Vol. 6, No. 12, pp. 1673-1687, 1997.
  6. Provos N. , "Defending against statistical Steganalysis", Proceedings of 10th Usenix Security Symposium, pp. 323-335, 2001.
  7. Westfeld A. , "F5 A steganographic algorithm: High capacity despite better Steganalysis", Proceedings of 4th International Information Hiding Workshop, Springer-Verlag, Vol. 2137, pp. 289-302, 2001.
  8. Mahdavi M. , Samavi Sh. , Zaker N. and Modarres-Hashemi M. , "Steganalysis Method for LSB Replacement Based on Local Gradient of Image Histogram", Iranian Journal of Electrical & Electronic Engineering, 2008.
  9. Fridrich, J. , Goljan, M. and. Du,R. "Reliable detection of LSB steganography in grayscale and color Images", Proceeding of ACM, Special Session on Multimedia Security and Watermarking, Otawwa, Canada, pp. 27-30, 2001.
  10. Westfeld A. and Pfitzman A. , "Attacks on steganographic systems", roceedings of 3rd International Information Hiding Workshop, Springer-Verlag, pp. 61-76, 1999.
  11. Fridrich J. , Goljan M. , and Du R. , "Steganalysis based on JPEG compatibility", Proceedings of Special Digital Watermarking Data Hiding, pp. 275-280, 2001.
  12. Fridrich J. , Du R. and Meng L. , "Steganalysis of LSB encoding in color images", Proceedings of IEEE International Conference on Multimedia, Vol. 3, pp. 1279-1282, 2000.
  13. Zhang T. and Ping X. , "A new approach to reliable detection of LSB steganography in natural images", Elsevier Journal of Signal Processing, Vol. 83, pp. 2085–2093, 2003.
  14. Sun Z, Hui M, Guan C," Steganalysis Based on Co-occurrence Matrix of Differential Image ", International Conference on Intelligent Information Hiding and Multimedia Signal Processing, PP. 1097-1100, 2008.
  15. Kekre H. B, Athawale A. A, PatkiS. A," Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence Matrix ", International Journal of Image Processing, (IJIP), Vol. 5, PP. 711-720, 2011.
  16. USC-SIPI available at http://sipi. usc. edu/database/index. php.
  17. BSD available at ttp://www. eecs. berkeley. edu/Research/Projects/CS/vision/grouping/fg.
Index Terms

Computer Science
Information Sciences

Keywords

Steganography Steganalysis Glcm Multi Layer Perceptron Neural Network

Powered by PhDFocusTM