CFP last date
20 January 2025
Reseach Article

SBHCS: Spike based Histogram Comparison Steganalysis Technique

by Sonam Chhikara, Parvinder Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 5
Year of Publication: 2013
Authors: Sonam Chhikara, Parvinder Singh
10.5120/13110-0433

Sonam Chhikara, Parvinder Singh . SBHCS: Spike based Histogram Comparison Steganalysis Technique. International Journal of Computer Applications. 75, 5 ( August 2013), 39-44. DOI=10.5120/13110-0433

@article{ 10.5120/13110-0433,
author = { Sonam Chhikara, Parvinder Singh },
title = { SBHCS: Spike based Histogram Comparison Steganalysis Technique },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 5 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number5/13110-0433/ },
doi = { 10.5120/13110-0433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:30.116274+05:30
%A Sonam Chhikara
%A Parvinder Singh
%T SBHCS: Spike based Histogram Comparison Steganalysis Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 5
%P 39-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Steganography is the art of secretly transferring of data and steganalysis is the art of detecting that hidden data embedded in cover media. In the past years many powerful and robust methods of steganography and steganalysis have been reported in the literature. In this present work, a Steganalysis technique for Histogram-Shifting Based Data Hiding is designed to detect hidden data by using spike generation and template matching. The proposed work analyzes the characteristics of histogram changes during data hiding procedure, and then uses these features to distinguish between stego and original image. The presented work perform the steganalysis in four steps: First, an input image is filtered by using perwitt operator for edge detection. Second, the spike image is divided into 8x8 blocks and then histogram is generated for each block. Third, histogram of each block of stego-image and original image is compared by using 5 similarity measures (norm distance, cosine distance, Euclidean distance, Chi-squared distance, Entropy distance). Fourth, Neural Network (NN) is trained as a classifier to discriminate stego image from original image. Experimental results indicate that the proposed steganalysis method is better than the method proposed by Der-Chyuan Lou et. al. [1] and can effectively detect stego image at low bit rates.

References
  1. Der-Chyuan Lou et. al. "Active steganalysis for histogram-shifting based reversible data hiding", Elsevier, Optics Communications 285 , 2012, pp. 2510–2518.
  2. J. Tian, IEEE Transactions on Circuits and Systems for Video Technology 13 (8)(Aug. 2003) 890.
  3. Z. Ni, Y. -Q. Shi, N. Ansari, W. Su, IEEE Transactions on Circuits and Systems for Video Technology 16 (3) (March 2006) 354.
  4. C. -C. Chang, C. -Y. Lin, IEEE Transactions on Information Forensics and Security 1 (4) (Dec. 2006) 493.
  5. D. M. Thodi, J. J. Rodriguez, IEEE Transactions on Image Processing 16 (3) (March 2007) 1057.
  6. M. Goljan and R. Du, Reliable Detection of LSB Steganography in Grayscale and Color Images, Proc. of the ACM Workshop on Multimedia and Security, Ottawa, Canada, October 5(2001), pp. 27-30.
  7. M. Goljan and T. Holotyak, Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072, San Jose, CA, January 16-19-2006, pp. 1-13.
  8. T. Holotyak, J. Fridrich, S. Voloshynovskiy, "Blind Statistical Steganalysis of Additive Steganography Using Wavelet Higher Order Statistics," 9th IFIP TC-6 TC-11 Conference on Communications and Multimedia Security, LNCS vol. 3677, Springer-Verlag, Berlin, 2005,pp. 273–274.
  9. Jing Dong , Tieniu Tan , "Blind Image Steganalysis Based On Run-Length Histogram Analysis", IEEE image processing conference, 2008 ,pp. 2064-2067. oct,2008.
  10. T. Pevný and P. Bas, IEEE Trans. on Info. Forensics and Security, vol. 5(2),2010, pp. 215–224.
  11. Hong Zhao, Hongxia Wang, Muhammad Khurram Khan, "Steganalysis for palette-based images using generalized difference image and color correlogram",Elsevier,Signal Processing ,vol. 91 ,2011,pp. 2595–2605.
  12. Zhongwei He, Wei Sun, Wei Lu, Hongtao Lu, "Digital image splicing detection based on approximate run length" ,Elsevier, Pattern Recognition Letters ,vol. 32, 2011, pp. 1591–1597.
  13. Der-Chyuan Lou, Chen-Hao Hu, Chao-Lung Chou, Chung-Cheng Chiu "Steganalysis of HMPD reversible data hiding scheme", Elsevier, Optics Communications ,vol. 284 ,2011 , pp. 5406–5414.
  14. J. Kodovský and V. Holub, IEEE Trans. on Info. Forensics and Security, vol. 7(2), 2012, pp. 432-44.
  15. J. Kodovský, IEEE Trans. on Info. Forensics and Security, vol. 7(3), 2012, pp. 868-882.
  16. M. -J. Lesot, M. Rifqi, H. Benhadda, International Journal of Knowledge Engineering and Soft Data Paradigms 1 (1) (2009) 63.
  17. S. -H. Cha, International Journal of Mathematical Models and Methods in Applied Sciences 1 (4) (2007) 300.
  18. D. Weken, M. Nachtegael, E. Kerre, Lecture Notes in Computer Science 2715 (2003) 396.
  19. X. -Y. Luo, D. -S. Wang, P. Wang, F. -L. Liu, Signal Processing 88 (9) (Sep. 2008) 2138.
  20. Tariq Al Hawi, Mahmoud Al Qutayari, Hassan Barada, Steganalysis attacks on stego images using stego-signatures and statistical image properties, in: TENCON 2004, Region 10 Conference, vol. 2, 2004, pp. 104–107.
Index Terms

Computer Science
Information Sciences

Keywords

Steganography Steganalysis Spikes Neural Network (NN).