M. Mohrekesh, S. Azizi, S. Samavi and S. Shirani "Hierarchical Watermarking Framework Based on Analysis of Local Complexity Variations," Submitted, October 2013.

Abstract

Increasing production and exchange of multimedia content has increased the need for better protection of copyright by means of watermarking.  Different methods have been proposed to satisfy the tradeoff between imperceptibility and robustness as two important characteristics in watermarking while maintaining proper data-embedding capacity.  Many watermarking methods use image independent set of parameters. Different images possess different potentials for robust and transparent hosting of watermark data.  To overcome this deficiency, in this paper we have proposed a new hierarchical adaptive watermarking framework.  At the higher level of hierarchy, complexity of an image is ranked in comparison with complexities of images of a dataset. For a typical dataset of images, the statistical distribution of block complexities is found. At the lower level of the hierarchy, for a single cover image that is to be watermarked, complexities of blocks can be found.  Local complexity variation (LCV) among a block and its neighbors is used to adaptively control the watermark strength factor of each block.  Such local complexity analysis creates an adaptive embedding scheme which results in higher transparency by reducing blockiness effects.  This two level hierarchy has enabled our method to take advantage of all image blocks to elevate the embedding capacity while preserving imperceptibility.  For testing the effectiveness of the proposed framework, contourlet transform (CT) in conjunction with discrete cosine transform (DCT) is used to embed pseudorandom binary sequences as watermark.  Experimental results show that the proposed framework elevates the performance the watermarking routine in terms of both robustness and transparency. 

 

Index TermsAdaptive watermarking, complexity­ assessment, framework, imperceptibility, robustness, strength factor.

Type: 
Journal
Date
Year: 
2013
Month: 
OCTOBER