An adaptive blind watermarking scheme utilizing neural network for synchronization  

作　　者：吴健珍 谢剑英 杨煜普 

机构地区：[1]Dept. of Automation, Shanghai University of Engineering Science [2]Dept. of Automation, Shanghai Jiaotong University

出　　处：《Journal of Harbin Institute of Technology(New Series)》2007年第2期281-286,共6页哈尔滨工业大学学报（英文版）

基　　金：the National High Technology Research and Development Program of China(Grant No. 2001AA422420-02).

摘　　要：An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image’s brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper, which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image＇ s brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust wa- termark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural net- work. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.

关 键 词：digital watermark image moment geometric attack DWT fuzzy clustering 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]