Damageless Digital Watermarking Using Complexvalued Artificial Neural Network

Authors

  • Rashidah Funke Olanweraju Faculty of Engineering International Islamic, University Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2010.9.6

Keywords:

Digital Watermarking, Complex Back Propagation Algorithm, Complex-Valued Data (CVD), Complex-Valued Neural Network (CVNN), Fast Fourier Transform (FFT)

Abstract

Several high-ranking watermarking schemes using neural networks have been proposed in order to make the watermark stronger to resist attacks. However, the current system only deals with real value data. Once the data become complex, the current algorithms are not capable of handling complex data. In this paper, a distortion-free digital watermarking scheme based on Complex-Valued Neural Network (CVNN) in transform domain is proposed. Fast Fourier Transform (FFT) was used to obtain the complex number (real and imaginary part) of the host image. The complex values form the input data of the Complex Back-Propagation (CBP) algorithm. Because neural networks perform best on detection, classification, learning and adaption, these features are employed to simulate the Safe Region (SR) to embed the watermark, thus, watermark are appropriately mapped to the mid frequency of selected coeffi cients. The algorithm was appraised by Mean Squared Error MSE and Average Difference Indicator (ADI). Implementation results have shown that this watermarking algorithm has a high level of robustness and accuracy in recovery of the watermark.

 

References

Abdallah, E. E., Hamza, A. B., & Bhattacharya, P. (2006). A robust block-based image watermarking scheme using fast hadamard transform and singular value decomposition. IEEE International Conference on Pattern Recognition, 3, 673–676. http://jict.uum.edu.my/

Aibinu, A. M., Salami, M. J. E., Shafie, A. A., & Najeeb, A. R. (2008). Increasing the accuracy and speed of convergence of an ARMA coefficient determination using neural network technique. World Assembly of Engineers, Scientists and Technologists International Conference on Computer System Engineering (ICCSE), 32, 196–202.

Amin, M. F., & Murase K. (2009). Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing, 72, 945–955.

Bansal A. E., & Bhadauria, S. S. (2005). Watermarking using neural network and hiding the trained network within the cover image. Journal of Theoretical and Applied Information Technology, 663–670.

Bender, W., Gruhl, D., Morimoto, N., &. Lu, A. (1996). Techniques for data hiding. IBM Systems Journal, 35, (3/4), 313–336,

Celik, M. U., Lemma, A. N., Katzenbeisser, S., & Veen, M. V. (2008). Lookup-table-based secure client-side embedding for spread-spectrum watermarks. IEEE Transactions on Information Forensics and Security, 3, (3), 475–487.

Chang, C. Y. (2005). The application of a full counterpropagation neural network to image watermarking. Networking, Sensing and Control, 993–998.

Chen, W. -Y., & Chen, C. -H. (2005). A robust watermarking scheme using phase shift keying with combination of amplitude boost and low amplitude block selection. Pattern Recognition, 38, (4), 587–598.

Choi, H. J., Seo, Y. H., Yoo, J. S., & Kim, D. W. (2008). Digital watermarking technique for holography interference patterns in a transform domain. Optical and Lasers in Engineering, 46, (4), 343–348.

Cox, I. J., Miller, M. L., & Bloom, J. A. (1999). Digital watermarking. Morgan Kaufmann. Journal of ICT, 9, pp: 111–

Gao, T., & Gu, Q. ( 2007). Reversible image authentication based on combination of reversible and LSB algorithm. Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS), 4425576, 636–639.

Ge, S., Gao, Y., & Wang, R. (2007). Least significant bit steganography http://jict.uum.edu.my/ detection with machine learning techniques. Conference on Knowledge Discovery in Data, Proceedings International Workshop on Domain Driven, 24–32.

Georgiou, G., & Koutsougeras, C. (1992). Complex domain backpropagation. IEEE Trans. On Circuits and Systems II, 39, (5), 330–334.

Ham, F. M., & Kostanic, I. (2002). Principles of neurocomputing for science & engineering. Singapore: Mc.GrawHill, 136–140.

Hanna, A. I., & Mandic, D. P. (2002). A normalised complex backpropagation algorithm. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 1, 977–980.

Haykin, S. (2008). Neural networks and learning machines (3rd ed.). Prentice Hall.

Jin, C., & Wang, S. (2007). Applications of a neural network to estimate watermark embedding strength. IEEE 8th International Workshop on Image Analysis for Multimedia Interactive Services, 68–68.

Hernandez, M. C., Miyatake M. N., & Meana, H.M.P. (2005). Analysis of a DFT-based watermarking algorithm. 2nd International Conference on Electrical and Electronics Engineering, 44–47.

Khan, A., Tahir, S. F., Majid, A., & Choi, T. S. (2008). Machine learning based adaptive watermark decoding in view of anticipated attack. Pattern Recognition, 41, 2594–2610.

Kim, T., &. Adali, T. (2002). Complex backpropagation neural network using elementary tran-scendental activation functions. Proceedings of IEEE ICASSP, 2.

Leung, H., & Haykin, S. (1991). The complex backpropagation algorithm. IEEE Trans. On Signal Proceedings 3, (9), 2101–2104. Journal of ICT, 9, pp: 111–

Li, X., & Wang, R. (2007). A video watermarking scheme based on 3D-DWT and neural network. Ninth IEEE International Symposium on Multimedia, 110–115.

Liu, X., Fang, K., and Liu, B. (2009). A synthesis method based on stability analysis for complex-valued Hopfield neural network. Proceedings of http://jict.uum.edu.my/ the 7th Asian Control Conference. Hong Kong, China, 1245–1250.

Lu, W., Sun, W., & Lu, H. (2009). Robust watermarking based on DWT and nonnegative matrix factorization, Computers & Electrical Engineering, 35, 183–188.

Luo, X., Liu, B., & Liu, F. (2005). Improved RS method for detection of LSB steganography. Lecture Notes in Computer Science, Computational Science and its Applications.

Majhi, B., & Shalabi, H. (2005). An improved scheme for digital watermarking using functional link artificial neural network. Journal of Computer Science, 1, (2), 169–174. Me, S. -C., Li, R. -H., Dang, H. –M., & Wang, Y. -K. (2002). Decision of image watermarking strength based on artificial neural-networks. Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’O2), 5, 2430–2434.

Ming, Z. Z., Rong-Yon, L., & Le, W. (2003). Adaptive watermark scheme with RBF neural networks. IEEE International Conference Neural Networks & Signal Processing, 2, 1517–1520.

Naoe, K., & Takefuji, Y. (2008). Damageless information hiding using neural network on YCbCr domain. International Journal of Computer Science and Network Security, 8 (9).

Nita, T. (2003). Orthogonal decision boundaries and generalization of complex-valued neural networks. I Complex-Valued Neural Networks: Theories and applications, (Ed). Akira Hirose, World Scientific., 7–28.

Olanrewaju, R. F., Khalifa, O. O., Abdalla, A., Aburas, A. A., Zeki, A. M. (2010). Watermarking in safe region of frequency domain using complex-valued neural network. Proceedings of International Conference on Computer and Communication Engineering. Journal of ICT, 9, pp: 111–

Olanrewaju, R. F., Aburas, A. A, Khalifa, O. O., & Abdalla, A. (2009) State-of-the-art application of artificial neural network in digital watermarking and the way forward. International Conference on Computing and Informatics, 233–237.

Palit, A. K., & Popovic, D. (2005). Computational intelligence in time series http://jict.uum.edu.my/ forecasting. Springer.

Parthasarath, A. K., & Kak, S. (2007). An improved method of content based image watermarking. IEEE Transactions on Broadcasting, 53, (2).

Sang, J., & Alam, M. S. (2008). Fragility and robustness of binary-phase-only-filter-based fragile/semi fragile digital image watermarking. IEEE Transactions on Instrumentation and Measurement, 57, 3, 595–606.

Schmidt, T., Rahnama, H., & Sadeghian, A. (2008). A review of applications of artificial neural networks in cryptosystems, World Automation Congress, 1–6.

Senthil, V., Bhaskaran, R. (2007). Wavelet based digital image watermarking with robustness against geometric attacks. International Conference on Computational Intelligence and Multimedia Applications, 4, 89–93.

Servetto, S. D., Podichuk, C. I., & Tamachandran, K. (1998). Capacity issues in digital image watermarking. International Conference on Image Processing, 1, 445–449.

Shi-Chun, M., Ren-Hou, L., Hong-Mei, D. & Yun-Kuan, W. (2002). Decision of image watermarking strength based on artificial neural-networks. IEEE Proceedings of the 9th International Conference on Neural Information Processing, 5, 2430–2434.

Shih Y. F., & Wu, Y. T. (2005). Robust watermarking and compression for medical images based on genetic algorithms. International Journal of Information Sciences, 175, 200–216.

Smith, S. W. (1999). The scientist and engineer’s guide to digital signal processing. Retrieve from http://www.dspguide.com/ch8/6.htm

Ting, G. C. W., Goi, B. M., & Heng, S. H. (2007). A fragile watermarking scheme protecting originator’s rights for multimedia service. Lecture Notes in Computer Science on Computational Science and Its Applications, 4705/2007, 644–454. Journal of ICT, 9, pp: 111–

Tsai, H. H. (2007). Decision-based hybrid image watermarking in wavelet domain using HVS and neural networks. In D. Liu et al. (Eds.): ISNN, Part III, 4493, 904–913.

Van., R. G., Tirkel, S. A. Z., & Osborene, C. F. (1994). A digital watermark. In Proceedings IEEE International Conference Image Processing, 2, http://jict.uum.edu.my/ 86–92.

Wen, X. B., Zhang, H., Xu, X. Q., & Quan, J. J. (2008). A new watermarking approach based on probabilistic neural network in wavelet domain. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 13, 4, 355–360.

Wong, H. W. P., & Au, O. C. (2003). A capacity estimation technique for JPEG to JPEG image watermarking. IEEE Transactions on Circuits and Systems for Video Technology, 13,8 746–752.

Wu, M., & Liu B. (2003). Data hiding in image and video: Part I—fundamental issues and solutions. IEEE, Transactions on Image Processing, 12, 16. 685–695.

Zain, J. M., & Fauzi Abdul, R.M. (2007). Evaluation of medical image watermarking with tamper detection and recovery (AW-TDR): Proceedings of the 29th Annual International Conference of the IEEE EMBS, 5661–5664, Lyon France.

Zhang, F., & Zhang, H. (2005). Applications of a neural network to watermarking capacity of digital image. Neurocomputing, 67, 345–349.

Zhang, F., Zhang, X., & Zhang, H. (2007). Digital image watermarking capacity and detection error rate. Pattern Recognition Letters, 28, 1–10.

Zhang, J., Wang, N., & Xiong, F. (2002). Hiding a logo watermark into the multiwavelet domain using neural networks. Proceedings of 14th IEEE International Conference on Tools with Artificial Intelligence, (ICTAI 02), 477–482.

Zhang, X. H., & Zhang, F. (2005). A blind watermarking algorithm based on neural network, International Conference on Neural Networks and Brain, 2, 1073–1076.

Kuttera, M., and Petitcolas, F. A. P. (2000). Fair evaluation methods for image watermarking systems. Journal Electron Imaging, 9, (445).

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Published

24-03-2010

How to Cite

Olanweraju, R. F. (2010). Damageless Digital Watermarking Using Complexvalued Artificial Neural Network. Journal of Information and Communication Technology, 9, 111-137. https://doi.org/10.32890/jict2010.9.6

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Identifiers DOI 10.32890/jict2010.9.6