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GA-Based Optimized Image Watermarking Method With Histogram and Butterworth Filtering
Abstract
The present paper proposes a new and significant method of optimization for digital image watermarking by using a combination of Genetic Algorithms (GA), Histogram and Butterworth filtering. In this proposed method, the histogram range selection of low frequency components is taken as a significant parameter which assists in bettering the imperceptibility and robustness against attacks. The tradeoff between the perceptual transparency and robustness is considered as an optimization puzzle which is solved with the help of Genetic Algorithm. As a result, the experimental outcomes of the present approach are obtained. These results are secure and robust to various attacks such as rotation, cropping, scaling, additive noise and filtering attacks. The peak signal to noise ratio (PSNR) and Normalized cross correlation (NC) are carefully analyzed and assessed for a set of images and MATLAB2016B software is employed as a means of accomplishing or achieving these experimental results.
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