Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

Pattern Recognition

Volumn 41, Issue 1, 2008, Pages 56-66

  Liu, Qingzhong ^a   Sung, Andrew H ^a   Chen, Zhongxue ^b   Xu, Jianyun ^c  

^a NEW MEXICO INSTITUTE OF MINING AND TECHNOLOGY   (United States)

^b SOUTHERN METHODIST UNIVERSITY   (United States)

^c MICROSOFT   (United States)

Author keywords

DENFIS; Image complexity; LSB matching; Steganalysis; SVMRFE

Indexed keywords

FUZZY INFERENCE; IMAGE CLASSIFICATION; RECURSIVE FUNCTIONS; SUPPORT VECTOR MACHINES;

GRAYSCALE IMAGES; IMAGE COMPLEXITY; STEGANALYSIS; STEGANOGRAPHY;

FEATURE EXTRACTION;

EID: 34548017677     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.06.005     Document Type: Article

References (35)

1. Fridrich J., Goljan M., Hogea D., and Soukal D. Quantitative steganalysis: estimating secret message length. ACM Multimedia Syst. J. (Special Issue on Multimedia Security) 9 3 (2003) 288-302
2. S. Dumitrescu, X. Wu, Z. Wang, Detection of LSB steganography via sample pair analysis, in: F.A.P. Petitcolas (Ed.), Information Hiding, Fifth International Workshop, Lecture Notes in Computer Science, vol. 2578, Springer, New York, 2002, pp. 355-372.
3. A. Ker, Improved detection of LSB steganography in grayscale images, in: J. Fridrich (Ed.), Information Hiding, Sixth International Workshop, Lecture Notes in Computer Science, vol. 3200, Springer, New York, 2005, pp. 97-115.
4. Liu Q., Sung A., Xu J., and Venkataramana V. Detect JPEG steganography using polynomial fitting. Proceedings of 16th Artificial Neural Networks in Engineering (2006), ASME Press, New York 547-556
5. J. Fridrich, M. Goljan, D. Hogea, Steganalysis of JPEG images: breaking the F5 algorithm, in: F.A.P. Petitcolas (Ed.), Information Hiding, Fifth International Workshop, Lecture Notes in Computer Science, vol. 2578, Springer, New York, 2002, pp. 310-323.
6. J. Harmsen, W. Pearlman, Steganalysis of additive noise modelable information hiding, in: E. Delp III, P. Wong (Eds.), Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents V, Proceedings of the SPIE, vol. 5020, 2003, pp. 131-142.
7. M. Choubassi, P. Moulin, A new sensitivity analysis attack, in: E. Delp III, P. Wong (Eds.), Security, Steganography, and Watermarking of Multimedia Contents, VII, Proceedings of SPIE-IS &T Electronic Imaging, vol. 5681, SPIE, 2005, pp. 734-745.
8. J. Fridrich, M. Goljan, Digital image steganography using stochastic modulation, in: E. Delp et al. (Eds.), Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents V, vol. 5020, 2003, pp. 191-202.
9. P. Moulin, A. Briassouli, A Stochastic QIM algorithm for robust, undetectable image watermarking, in: Proceedings of ICIP, vol. 2, 2004, pp. 1173-1176.
10. T. Sharp, An implementation of key-based digital signal steganography, in: I. Moskowitz (Ed.), Information Hiding, Fourth International Workshop, Lecture Notes in Computer Science, vol. 2137, Springer, New York, 2001, pp. 13-26.
11. Ker A. Steganalysis of LSB matching in grayscale images. IEEE Signal Process. Lett. 12 6 (2005) 441-444
12. S. Lyu, H. Farid, Steganalysis using color wavelet statistics and one-class support vector machines, in: SPIE Symposium on Electronic Imaging, San Jose, CA, 2004.
13. Lyu S., and Farid H. How realistic is photorealistic. IEEE Trans. Signal Process. 53 2 (2005) 845-850
14. J. Fridrich, D. Soukal, M. Goljan, Maximum likelihood estimation of length of secret message embedding using ± K steganography in spatial domain, in: E. Delp III, P. Wong (Eds.), Security, Steganography, and Watermarking of Multimedia Contents, VII, Proceedings of SPIE-IS &T Electronic Imaging, vol. 5681, SPIE, 2005, pp. 595-606.
15. T. Holotyak, J. Fridrich, S. Voloshynovskiy, Blind statistical steganalysis of additive steganography using wavelet higher order statistics, Proceedings of the 9th IFIP TC-6 TC-11 Conference on Communications and Multimedia Security, September 19-21, 2005.
16. Q. Liu, A. Sung, J. Xu, B.Ribeiro, Image complexity and feature extraction for steganalysis of LSB matching steganography, in: Proceedings of the 18th International Conference on Pattern Recognition, ICPR, vol. 2, 2006, pp. 267-270.
17. Avcibas I., Memon N., and Sankur B. Steganalysis using image quality metrics. IEEE Trans. Image Process. 12 2 (2003) 221-229
18. Kasabov N., and Song Q. DENFIS dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10 2 (2002) 144-154
19. Kasabov N. Evolving Connectionist Systems Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines (2002), Springer, London, New York
20. Guyon I., Weston J., Barnhill S., and Vapnik V. Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. 46 1-3 (2002) 389-422
21. J. Huang, D. Mumford, Statistics of natural images and Models, in: Proceedings on Computer Vision and Pattern Recognition, vol. 1, June 23-25, 1999.
22. Srivastava A., Lee A., Simoncelli E.P., and Zhu S. On advances in statistical modeling of natural images. J. Math. Imaging Vision 18 1 (2003) 17-33
23. Winkler G. Image Analysis, Random Fields and Dynamic Monte Carlo Methods (1996), Springer, New York
24. Sharifi K., and Leon-Garcia A. Estimation of shape parameter for generalized Gaussian distributions in sub-band decompositions of video. IEEE Trans. Circuits Syst. Video Technol. 5 (1995) 52-56
25. Wouwer G., Scheunders P., and Dyck D. Statistical texture characterization from discrete wavelet representations. IEEE Trans. Image Process. 8 (1999) 592-598
26. Moulin P., and Liu J. Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors. IEEE Trans. Inf. Theory 45 (1999) 909-919
27. T. Takagi, M., Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man, Cyberne. (1985) 116-132.
28. J. Yu, X. Chen, Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data, Bioinformatics, 21(suppl.1), (2005) pp. i487-i494.
29. Pvlidis P., and Noble W. Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol. 2 10 (2001) 0042.1-0042.15
30. Inza I., Sierra B., Blanco R., and Larranaga P. Gene selection by sequential search wrapper approaches in microarray cancer class prediction. J. Intelligent Fuzzy Syst. 12 1 (2002) 25-33
31. H., Motulsky, Intuitive Biostatistics, Oxford University Press, Oxford, 1995, ISBN 0-19-508607-4.
32. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
33. Schlesinger M., and Hlavac V. Ten Lectures on Statistical and Structural Pattern Recognition (2002), Kluwer Academic Publishers, Dordrecht
34. Heijden F., Duin R., Ridder D., and Tax D. Classification, Parameter Estimation and State Estimation (2004), Wiley, New York
35. Schapire R., and Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37 3 (1999) 297-336