American Institute of Mathematical Sciences

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December  2015, 8(6): 1291-1299. doi: 10.3934/dcdss.2015.8.1291

Characteristic analysis of carrier based on the filtering and a multi-wavelet method for the information hiding

Shuai Ren 1, , Tao Zhang 2, and  Fangxia Shi 3,

1. 

School of Information Engineering, Chang'an University, Xi'an, Shaanxi 710064, China
2. 

School of Electronic and Control Engineering, Chang'an University, Xi'an, Shaanxi 710064, China
3. 

School of Information Engineering, Xizang Minzu University, Xianyang, Shaanxi 712082, China

Received  June 2015 Revised  September 2015 Published  December 2015

The characteristic consistency between the original and the stego carriers is an important indicator to evaluate an information hiding algorithm. Different from the traditional carrier pre-processing methods which are based on the operation domains, we propose a characteristics analysis-based preprocessing scheme. We use the Gaussian pyramid filtering and CARDBAL2 multi-wavelet transform to analyze the energy characteristics of the carrier, so the original carrier can be decomposed into several sub-regions with different energy level. And at the same time, the processed carrier shows us the redundancy space structurally through the combination bit plane method, which can provide some invisible hiding positions. Obviously, the energy and structure characteristics are at least related with the robustness and invisibility of the hiding result respectively. So we can improve these performances compared with the traditional methods. At the same time, some optimization theories like the Chebyshev map are used to improve other performances. At last, the experimental shows the achievements of this scheme in the form of data.
Keywords: Gaussian pyramid, genetic algorithm., Information hiding, CARDBAL2 multi-wavelet transform, Chebyshev map.
Mathematics Subject Classification: Primary: 94A62, 68Q30; Secondary: 94A0.
Citation: Shuai Ren, Tao Zhang, Fangxia Shi. Characteristic analysis of carrier based on the filtering and a multi-wavelet method for the information hiding. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1291-1299. doi: 10.3934/dcdss.2015.8.1291
References:
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show all references

References:
[1]

A. Basu, A. Saha, J. Das, S. Roy, S. Mitra and I. Mal, On the implementation of a digital watermarking based on phase congruency, Advances in Intelligent Systems & Computing, 328 (2014), 113-120. doi: 10.1007/978-3-319-12012-6_13.  Google Scholar

[2]

L. Cui and W. Li, Adaptive multiwavelet-based watermarking through JPW masking, IEEE Transactions on Image Processing, 20 (2011), 1047-1060. doi: 10.1109/TIP.2010.2079551.  Google Scholar

[3]

A. Durdek, S. R. Jensen and J. Juselius, et al., Adaptive order polynomial algorithm in a multiwavelet representation scheme, Applied Numerical Mathematics, 92 (2015), 40-53. doi: 10.1016/j.apnum.2014.12.006.  Google Scholar

[4]

K. Jafari-Khouzani, Multiwavelet grading of pathological images of prostate, IEEE Trans. Biomed. Eng., 50 (2003), 697-704. doi: 10.1109/TBME.2003.812194.  Google Scholar

[5]

J. Kim, M. Yi and M. S. Obaidat, Advanced computer mathematics based cryptography and security technologies, International Journal of Computer Mathematics, 90 (2013), 2512-2514. doi: 10.1080/00207160.2013.868728.  Google Scholar

[6]

Y. Kutlu, Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients, Comput. Methods Programs Biomed., 105 (2012), 257-267. doi: 10.1016/j.cmpb.2011.10.002.  Google Scholar

[7]

Y. Li, H. L. Wei and S. A. Billings, Identification of time-varying systems using multi-wavelet basis functions, Control Systems Technology IEEE Transactions on, 19 (2011), 656-663. doi: 10.1109/TCST.2010.2052257.  Google Scholar

[8]

X. B. Ren, An optimal image thresholding using genetic algorithm, Proceedings of the 2009 International Forum on Computer Science-Technology and Applications, 1 (2009), 169-172. doi: 10.1109/IFCSTA.2009.48.  Google Scholar

[9]

B. Stoyanov and K. Kordov, Novel image encryption scheme based on chebyshev polynomial and duffing map, The Scientific World Journal, 2014 (2014), Article ID 283639, 11 pages. doi: 10.1155/2014/283639.  Google Scholar

[10]

M. Saini and R. Chhikara, Performance evaluation of DCT and DWT features for blind image steganalysis using neural networks, International Journal of Computer Applications, 114 (2015), 20-23. doi: 10.5120/19974-1868.  Google Scholar

[11]

A. R. Tate, N. Beloff and A. R. Balques, et al., Exploiting the potential of large databases of electronic health records for research using rapid search algorithms and an intuitive query interface, Applied Numerical Mathematics, 21 (2014), 292-298. doi: 10.1136/amiajnl-2013-001847.  Google Scholar

[12]

L. Yang, J. Lv and Y. Xiang, Underdetermined blind source separation by parallel factor analysis in time-frequency domain, Cognitive Computation, 5 (2013), 207-214. doi: 10.1007/s12559-012-9177-9.  Google Scholar

[13]

T. Zhang, D. J. Mu and S. Ren, A confidential communication-oriented information hiding algorithm based on GHM multi-wavelet and DCT, Applied Mathematics & Information Sciences, 7 (2013), 1803-1807. doi: 10.12785/amis/070518.  Google Scholar

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