Abstract

A scheme is proposed to hide data in images based on a prioritized ordering of the content of the host (or cover) image. A watermark embedding process uses the watermark strength to determine the ordering of the 16 regions resulting from the second level Discrete Wavelet Transform (DWT) decomposed content of the host image. To determine the best ordering for hiding the data, various types of image of varying content and, hence, visual complexity were considered, analyzed and ranked in terms of their ability to withstand changes that do not imperil the visual quality (PSNR) of their watermarked versions depending on which an N×N-sized watermark stream is hidden in the 8 highest ranked sub-bands of the host image. From this perspective, an ordering for images classified as simple, normal and complex images was used to determine a generalized ordering of the DWT decomposed sub-bands of the image. The generalized ordering presented here ascertains that the content of the image and its visual complexity had little effect on an earlier proposed prioritized ordering of the DWT Sub-bands. To validate the veracity of the ordering scheme, 1000 images from the Corel 1000A database, their visual complexity and features were taken into account. The results confirmed that high embedding capacity, appreciable visual quality of watermarked images and complete recovery hidden data are realizable based on the ordering scheme proposed in this study.

Keywords:

Data hiding, Discrete Wavelet Transform (DWT), image processing, information security, visual complexity, watermarking,

References

1. Al-Asmari, A.K., M.A. Al-Qodah and A.S. Salama, 2011. Wavelet-pixel value differencing technique for digital images data hiding. Proceeding of the IEEE 2011 International Conference on System Engineering and Technology (ICSET), pp: 15-18.
   CrossRef    
2. Al-Asmari, A.K., A.S. Salama, A.M. Iliyasu and M.A. Al-Qodah, 2012. A DWT ordering scheme for hiding data in images based on pixel value difference. Proceeding of the 8th International Conference on Computational Intelligence and Security (CIS). Ghoungou, China, pp: 553-557.
   
3. Cardici, M., V.D. Gesu, M. Petrou and M.E. Tabacchi, 2009. A fuzzy approach to the evaluation of image complexity. Fuzzy Set. Syst., 60(10): 1474-1484.
   CrossRef    
4. Cui, L. and L. Li, 2011. Adaptive multiwavelet-based watermarking through JPW masking. IEEE T. Image Process., 20(4): 1047-1060.
   CrossRef    PMid:20876023    
5. Elbasi, E. and A.M. Eskicioglu, 2006. A semi-blind watermarking scheme for color images using a tree structure. Proceeding of the IEEE Sarnoff Symposium, Western New York Image Processing Workshop, Rochester Institute of Technology, Rochester, pp: 5-8.
   
6. Griffin, G., A.D. Holub and P. Perona, 2013. The Caltech-256. Caltech Technical Report. (Accessed on: Sept., 2013).
   Direct Link
7. Iliyasu, A.M., 2013a. Towards realising secure and efficient image and video processing applications on quantum computers. Entropy, 15(8): 2874-2974.
   CrossRef    
8. Iliyasu, A.M., P.Q. Le, F. Dong and K. Hirota, 2012. Watermarking and authentication of quantum images based on restricted geometric transformations. Inform. Sci., 186(1): 126-149.
   CrossRef    
9. Iliyasu, A.M., A.K. Al-Asmari, M. Abdel Wahab, A.S. Salama, M.A. Al-Qodah, P.Q. Le and F. Yan, 2013b. Mining visual complexity of images based on an enhanced feature space representation. Proceeding of the 8th IEEE International Symposium on Intelligent Signal Processing (WISP 2013), pp: 65-70.
   CrossRef    
10. Kim, J.R. and Y.S. Moon, 1999. A robust wavelet-based digital watermarking using level-adaptive thresholding. Proceeding of the IEEE International Conference on Image Processing (ICIP, 1999), 2: 226-230.
    
11. Le, P.Q., A.M. Iliyasu, J.A.G. Sanchez, F. Dong and K. Hirota, 2011. A three dimensional feature space for representing visual complexity of images using their structure, noise and diversity. Proceeding of the 12th International Symposium on Advanced Intelligent Systems (ISIS, 2011), pp: 417-420.
    
12. Le, P.Q., A.M. Iliyasu, J.A.G. Sanchez, F. Dong and K. Hirota, 2012. Representing visual complexity of images using a 3D feature space based on structure, noise and diversity. J. Adv. Comput. Intell. Intell. Inform., 16(5): 631-637.
    CrossRef    
13. Makhoghi, M., F. Akhlaghian and H. Danyali, 2010. Robust digital image watermarking using singular value decomposition. Proceeding of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT, 2010), pp: 219-224.
    
14. Mario, M. Chacon, D. Alma and S. Corral, 2005. Image complexity measure: A human criterion free approach. Proceeding of Annual Meeting of the North American the Fuzzy Information Processing Society (NAFIPS, 2005), pp: 241-246.
    
15. Rigau, J., M. Feixas and M. Sbert, 2005. An information-theoretic framework for image complexity. Proceedings of the 1st Eurographics Conference on Computational Aesthetics in Graphics Visualization and Imaging (Computational Aesthetics'05), pp: 177-184.
    
16. Salama, A.S., R. Atta, R. Rizk and F. Wanes, 2011. A robust digital image watermarking technique based on wavelet transform. Proceeding of the IEEE International Conference on System Engineering and Technology (ICSET), pp: 100-105.
    CrossRef    
17. Salama, A.S., M.A. Al-Qodah, A.M. Iliyasu, A.K. Al-Asmari and F. Yan, 2013. A Hybrid Fusion Technique for Watermarking Digital Images. In: Swiatekm J. (Ed.), Advances in Systems Science. Advances in Intelligent Systems and Computing. Springer, DOI: 10.1007/978-3-319-01857-7_20, pp: 207-217.
    CrossRef    
18. Tao, P. and A.M. Eskicioglu, 2004. A robust multiple watermarking scheme in the DWT domain, optics east. Proceeding of the Symposium on Internet Multimedia Management Systems V Conference. Philadelphia, pp: 133-144.
    CrossRef    PMid:15495802    
19. Wang, X. and H. Zhao, 2006, A novel synchronization invariant audio watermarking scheme based on DWT and DCT. IEEE T. Signal Process., 54: 12-22.
    
20. Wang, C., X. Li and B. Yang, 2010. Efficient reversible image watermarking by using dynamical prediction-error expansion. Proceeding of the 17th IEEE International Conference on Image Processing (ICIP, 2010), pp: 3673-3676.
    CrossRef    
21. Weng, S., Y. Zhao and J. Pan, 2008. A novel reversible data hiding scheme. Int. J. Innov. Comput. I., 4(2): 351-358.
    
22. Yaghmaee, F. and M. Jamzad, 2005. Computing watermark capacity in images according to their quad tree. Proceeding of the 5th IEEE International Symposium on Signal Processing and Information Technology, pp: 823-826.
    
23. Yaghmaee, F. and M. Jamzad, 2010. Estimating watermarking capacity in gray scale images based on image complexity. EURASIP J. Adv. Sig. Pr., 2010: 9. Article ID 851920, DOI: 10.1155/2010/851920.
    CrossRef    
24. Zhang, F., X. Zhang and H. Zhang, 2008. Digital image watermarking capacity and detection error rate. Pattern Recogn. Lett., 28: 1-10.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.