Home   >   CSC-OpenAccess Library   >    Manuscript Information
ANOVA and Fisher Criterion based Feature Selection for Lower Dimensional Universal Image Steganalysis
Madhavi Bharatbhai Desai, S. V. Patel, Bhumi Prajapati
Pages - 145 - 160     |    Revised - 30-06-2016     |    Published - 31-07-2016
Volume - 10   Issue - 3    |    Publication Date - July 2016  Table of Contents
Steganalysis, SVM, ANOVA, Fisher Criterion, DCT, DWT, Dimensionality Reduction.
Unethical uses of data hiding methods have made Image Steganalysis a very important area of research work in the field of Digital Investigations. Effectiveness of any Image Steganalysis algorithm depends on feature selection and feature reduction. The goal of this paper is to develop a reduced dimensional merged feature set for universal image steganalysis using Fisher Criterion and ANOVA techniques. Statistical features extracted from wavelet subbands and binary similarity patterns extracted from DCT of an image are merged to make combined feature set. Fisher criterion and ANOVA test are applied to evaluate the combined feature vector score and then only those features are selected which are found sensitive in both feature selection methods. These reduced dimensional 15-D feature vector is used to train SVM classifier with RBF kernel. The proposed algorithm is tested against steganography methods like F5, Outguess and LSB based method. Stego images are generated using widely available stego tools for two standard image databases: CorelDraw and BSDS500. Results are further validated using 10 fold cross validation process. The proposed algorithm achieves overall 97% detection accuracy against various steganography methods.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
CorelDraw Database: http://www.corel.com.
D. Zou, Y. Q. Shi, W. Su and G. Xuan. “Steganalysis based on markov model of threshold prediction-error image.” in Proc. of the 2012 IEEE Int. Conf. on Multimedia and Expo. Toronto, Canada, 2006, pp. 1365-1368.
G. Xuan. “Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions.” in Lecture Notes in Computer Science, 3727, Springer-Verlag, Berlin, pp.262–277, 2005.
H. Farid. “Detecting hidden messages using higher-order statistical models.” in Proc. IEEE Int. Conf. Image Processing, Rochester, NY, 2002, pp. 905-908.
I. Avcibas, M. Kharrazi, N. Memon and B. Sankur. “Image steganalysis with binary similarity measures.” EURASIP Journal on Applied Signal Processing, pp.2749–2757, 2005.
J. Fridrich. “Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes.” in Proc. of Information Hiding Workshop, Lecture Notes in Computer Science, Springer, 3200, 2004, pp.67–81.
J. J. Harmsen. “Steganalysis of additive noise modelable information hiding.” Master Thesis of Rensselaer Polytechnic Institute, Troy, New York, 2003.
J. Kodovsky, J. Fridrich and V. Holub. “Ensemble classifier for steganalysis of digital media.” IEEE Trans. Inf. Forensics Security, vol. 7(2), pp.432–444, 2012.
J.-C Lu, F.-L. Liu and X.-Y. Luo. “Selection of image features for steganalysis based on the Fisher criterion.” Digital Investigation, vol. 11(1), pp. 57–66, 2014.
Jing-Qu Lin and Shang-Ping Zhong. “Jpeg image steganalysis method based on binary similarity measures.” in Proc. of Eighth International Conference on Machine Learning and Cybernetics, Baoding, July 2009, pp. 2238-2243.
K. Sullivan, U. Madhow, S. Chandrasekaran and B. S. Manjunath.”Steganalysis of spread spectrum data hiding exploiting cover memory.” SPIE, pp.38-46, 2005.
Outguess: http://www.outguess-rebirth.com/.
R. Lakshmi Priya, P. Eswaran and S. L Ponnambli Kamakshi. “Blind Steganalysis with Modified Markov Features and RBFNN.” IJERT, vol. 2(5), pp. 2278-0181, 2013.
S. Liu, L. Ma, H. Yao and D. Zhao. “Universal steganalysis based on statistical models using reorganization of block-based dct coefficients.” presented at fifth Int. Conf. Information Assurance and Security,2009.
S. Lyu and H. Farid. “Detecting hidden messages using higher-order statistics and support vector machine.” Lecture Notes in Information Hiding, Springer Berlin Heidelberg, pp.340-354, 2003.
S. Lyu and H. Farid. “Steganalysis using color wavelet statistics and one-class vector support machines.” in Proc. of SPIE Security, Steganography, Watermarking of Multimedia Contents, 2004, pp.35–45.
S. S. Ekhande, S. P. Sonavane and P. J. Kulkarni. “Universal steganalysis using feature selection strategy for higher order image statistics,” International Journal of Computer Applications, vol. 1(19), pp. 52-55, 2010.
T. Penvy and J. Fridrich. “Merging Markov and dct features for multi-class JPEG Steganalysis.” in Proc. of SPIE, San Jose, CA, 2007.
V. Batagelj and M. Bren. “Comparing resemblance measures,” in Proc. International Meeting on Distance Analysis (DISTANCIA’92), Rennes, France, June,1992.
Wing W. Y. NG, Zhi-Min He, Patrick P.K. Chan and Daniel S. Yeung. “Blind steganalysis with high generalization capability for different image databases l-gem.” in Proc. of the 2011 Int. Conf. on Machine Learning and Cybernetics, Guilin, July 2011, pp. 1690-1695.
X. Luo, F. Liu, J. Chen and Y. Zhang. “Image universal steganalysis based on wavelet packet transform,” Multimedia Signal Processing, IEEE 10th Workshop on Digital, pp.780 – 784, 2008.
Y. Q. Shi, C. Chen and W. Chen. “A markov process based approach to effective attacking jpeg steganography.” in Proc. of the 8th International Workshop, Springer, Berlin, 2006, pp. 249-264.
Y. Q. Shi, G. Xuan, C. Yang, G. Gao, Z. Zhang and P. Chai. “Effective steganalysis based on statistical moments of wavelet characteristic function” in Proc. of the Int. Conf. on Information Technology: Coding and Computing, 2005, pp. 768-773.
Y. Q. Shi, G. Xuan, D. Zou and J. Gao. “Steganalysis based on moments of characteristic function using wavelet decomposition, prediction error image and neural network.” in Proc. of IEEE ICME, 2005, pp. 269-272.
Y. Wang and P. Moulin. “Optimized feature extraction for learning based image steganalysis,” IEEE Trans in Forensics Security, vol. 2(1), pp. 262-277, 2005.
Mrs. Madhavi Bharatbhai Desai
Uka Tarsadia University - India
Dr. S. V. Patel
Sarvajanik College of Engineering and Technology - India
Miss Bhumi Prajapati
Sarvajanik College of Engineering and Technology - India

View all special issues >>