Optimal Parameter Selection for Image Watermarking Using MOGA

The notion of the proposed methodology is to optimize multidimensional nonlinear problem of conflicting nature that exists among imperceptibility and robustness in image watermarking. The methodology exploits the potentiality of Multi-Objective Genetic Algorithm (MOGA) in searching multiple non-dominated solutions lying on the Pareto front. The characteristics curve of the image are then analyzed and the most appropriate solution is selected using a merit function defined over evaluation measures. The efficacy of the suggested method is demonstrated by reporting the resultant watermarked images and restored watermarks extracted from their mean and median filtered versions.

[1]  Stephan Katzenbeisser,et al.  Information Hiding Techniques for Steganography and Digital Watermaking , 1999 .

[2]  J. O'Ruanaidh,et al.  Rotation, Translation and Scale Invariant Digital Image Watermarking , 1997 .

[3]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[4]  Hsiang-Cheh Huang,et al.  Progressive Watermarking Techniques Using Genetic Algorithms , 2007 .

[5]  C. A. Murthy,et al.  Image Enhancement Using Multi-objective Genetic Algorithms , 2009, PReMI.

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  L. B. Milstein,et al.  Theory of Spread-Spectrum Communications - A Tutorial , 1982, IEEE Transactions on Communications.

[8]  X G Xia,et al.  Wavelet transform based watermark for digital images. , 1998, Optics express.

[9]  Deepa Kundur,et al.  A robust digital image watermarking method using wavelet-based fusion , 1997, Proceedings of International Conference on Image Processing.

[10]  Malay Kumar Kundu,et al.  Genetic algorithms for optimality of data hiding in digital images , 2008, Soft Comput..

[11]  Walter Bender,et al.  Techniques for data hiding , 1995, Electronic Imaging.

[12]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[13]  M. Hansen,et al.  Evaluating the quality of approximations to the non-dominated set , 1998 .

[14]  Michael P. Ekstrom,et al.  Digital Image Processing Techniques , 1984 .