Research Article
Performance Evaluation of First and Second Order Features for Steganalysis
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 92 - Issue 16 |
| Published: April 2014 |
| Authors: Ashu, Rita Chhikara |
10.5120/16093-5372
|
Ashu, Rita Chhikara . Performance Evaluation of First and Second Order Features for Steganalysis. International Journal of Computer Applications. 92, 16 (April 2014), 17-22. DOI=10.5120/16093-5372
@article{ 10.5120/16093-5372,
author = { Ashu,Rita Chhikara },
title = { Performance Evaluation of First and Second Order Features for Steganalysis },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 92 },
number = { 16 },
pages = { 17-22 },
doi = { 10.5120/16093-5372 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Ashu
%A Rita Chhikara
%T Performance Evaluation of First and Second Order Features for Steganalysis%T
%J International Journal of Computer Applications
%V 92
%N 16
%P 17-22
%R 10.5120/16093-5372
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper we have extracted various features from the images and evaluate their performance for various steganography tools with different classifier like J48, SMO, Naive bayes's . There are so many steganography tools and many of them are changing the original images statistically during embedding process. To calculate that changes we are extracting various features from the cover image and stego image in spatial domain as well as DCT domain of JPEG image. Then train the classifier with the calculated feature vector of cover and stego image and evaluate features, that are more accurate to detect the hidden message. In this paper, we are using the three Steganography tools – nsf5 , PQ ,JPH&S with their different embedding efficiency like 10,25,50,100 percent .
References
- J. Fridrich, M. Goljan, R. Du, Detecting LSB Steganography in color and gray-scale images, IEEE Multimedia Magaz. , Special Issue on Security (October– November 2001) 22–28.
- N. Johnson, S. Jajodia, Exploring Steganography: Seeing the unseen, IEEE Comput. 31 (2) (1998) 26–34.
- Arooj Nissar , A. H. Mir : Classification of steganalysis techniques: A study
- Westfeld, A. and Pfitzmann, A. : Attacks on Steganographic Systems. In: Pfitzmann A. (eds. ): 3rd International Workshop. Lecture Notes in Computer Science, Vol. 1768. Springer-Verlag, Berlin Heidelberg New York (2000) 61?75
- Fridrich, J. , Goljan, M. , Hogea, D. , and Soukal, D. : Quantitative Steganalysis: Estimating Secret Message Length. ACM Multimedia Systems Journal. Special issue on Multimedia Security, Vol. 9(3) (2003) 288–302
- Farid H. and Siwei, L. : Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines. In: Petitcolas, F. A. P. (ed. ): Information Hiding. 5th International Workshop. Lecture Notes in Computer Science, Vol. 2578. Springer-Verlag, Berlin Hei-delberg. NewYork(2002)340–354.
- J. Fridrich, "Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes," in Information Hiding. Springer, 2004, pp. 67–81.
- S. Lyu and H. Farid, "Detecting hidden messages use higher-order statistics and support vector machines," in Information Hiding. Springer, pp. 340–354.
- Y. Shi, C. Chen, and W. Chen, "A Markov process based approach to effective attacking JPEG Steganography," in Information Hiding. Springer, 2006, pp. 249–264.
- Qingzhong Liu , Andrew H. Sung , Mengyu Qiao , Zhongxue Chen ,and Bernadette Ribeiro , "An improved approach to steganalysis of JPEG Images"
- A. Latham. (1999, August) Jp hide&seek. [Online]. Available: http://linux01. gwdg. de/~alatham/stego. html
Index Terms
Keywords