Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn
DOI:
https://doi.org/10.25271/sjuoz.2019.7.1.574Keywords:
Steganalysis, Co-Occurrence matrix, DWT, DFT, DCT, BPNNAbstract
In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works.
References
[2] S. M. Badr and A. H. Khalil, “A Review on Steganalysis Techniques : From Image Format Point of View,” Int. J. Comput. Appl., vol. 102, no. 4, pp. 11–19, 2014.
[3] S. O. Hasson and F. M. Khalifa, “Steganalysis Using KL Transform and Radial Basis Neural Network,” Raf. J. Comp. Math’s., vol. 9, no. 1, pp. 47–58, 2012.
[4] M. Bachrach and F. Y. Shih, “Image steganography and steganalysis,” Wiley Interdiscip. Rev. Comput. Stat., vol. 3, no. 3, pp. 251–259, 2011.
[5] J. Davidson and C. Bergman, “An Artificial Neural Network for Wavelet Steganalysis,” Final Rep. to Midwest Forensics Resource. Cent., pp. 1–23, 2005.
[6] V. P. Venkatesan, “Steganalysis Using Colour Model Conversion,” SIPIJ, vol. 2, no. 4, pp. 201–211, 2011.
[7] R. Gong and H. Wang, “Steganalysis for GIF images based on colors-gradient co-occurrence matrix,” Opt. Commun., vol. 285, no. 24, pp. 4961–4965, 2012.
[8] A. Aljarf, S. Amin, J. Filippas, and J. Shuttelworth, “Develop a Detection System for Grey and ColourStego Images,” Int. J. Model. Optim., vol. 3, no. 5, pp. 3–6, 2013.
[9] J. Zeng, S. Member, S. Tan, S. Member, B. Li, and S. Member, “Large-scale JPEG image steganalysis using hybrid,” vol. 6013, no. c, pp. 1–15, 2017.
[10] M. Kaur and G. Kaur, “Review of Various Steganalysis Techniques,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 2, pp. 1744–1747, 2014.
[11] C. G. Eichkitz, J. Davies, J. Amtmann, and M. G. Schreilechner, “Grey level co-occurrence matrix and its application to seismic data,” First Break, vol. 33, no. 3, pp. 71–77, 2015.
[12] C. Di Ruberto, G. Fodde, and L. Putzu, “On Different Colour Spaces for Medical Colour Image Classification,” in International Conference on Computer Analysis of Images and Patterns, pp. 477–488, 2015.
[13] Z. I. Rasool, “The Detection of Data Hiding in RGB Images Using Statistical Steganalysis,” M. Sc. Thesis, Middle East University, 2017.
[14] B. S. V, A. Unnikrishnan, and K. Balakrishnan, “Grey Level Co-occurrence Matrices : Generalisation and Some New Features,” Int. J. Comput. Sci. Eng. Inf. Technol., vol. 2, no. 2, pp. 151–157, 2012.
[15] P. Balakrishnan, “Design and Implementation of Lifting Based Daubechies Wavelet Transform Using Algebraic Integers,” M. Sc. Thesis, University of Saskatchewan, 2013.
[16] Shi, Yun Q., Guorong Xuan, Dekun Zou, Jianjiong Gao, Chengyun Yang, Zhenping Zhang, Peiqi Chai, Wen Chen, and Chunhua Chen. “Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network,” IEEE Int. Conf. Multimedia. Expo, ICME, pp. 269–272, 2005.
[17] Xuan, GR & Shi, Y.Q. & Gao, JJ & Zou, D & Yang, CY & Zhang, ZP & Chai, PQ & Chen, CH & Chen, Wen. "Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions." 7th International Workshop on Information Hiding, Vol. 3727, pp. 262-277, 2005.
[18] Shi, Yun Q., Guorong Xuan, Chengyun Yang, Jianjiong Gao, Zhenping Zhang, Peiqi Chai, Dekun Zou, Chunhua Chen, and Wen Chen. “Effective Steganalysis Based on Statistical Moments of Wavelet Characteristic Function,” in IEEE International Conference on Information Technology: Coding and Computing, vol. 1, pp. 768–773, 2005.
[19] Desai, Madhavi B., and S. V. Patel. "Performance analysis of image steganalysis against message size, message type and classification methods." In IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), pp. 295-302. IEEE, 2016.
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