{"title":"基于小波变换域中能量方案的图像水印算法","authors":"Jinhua Liu","doi":"10.1109/ICIVC.2018.8492868","DOIUrl":null,"url":null,"abstract":"With the increasing demands of copyright protection, digital watermarking has been paid more and more attention. In the design of a watermarking method, the modeling of signal by a general parametric family of statistical distributions plays an important role in many image watermarking applications. In this paper, the probability density function of wavelet coefficients is modeled by the generalized Gaussian distribution (GGD), and the decision threshold is obtained by the Neyman-Pearson (NP) criterion. In the procedure of watermark embedding, the energy of image block is considered in the watermark embedding. Only those blocks whose energy exceeds a predetermined threshold are used to embed the watermark data. Its improved robustness is due to embedding in the significant wavelet coefficients based on the energy scheme and control of its strength factor from the variance of coefficient. Experimental results demonstrate that the effectiveness of the presented watermarking and its robustness against common image processing and some kinds of geometric attacks.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"An Image Watermarking Algorithm Based on Energy Scheme in the Wavelet Transform Domain\",\"authors\":\"Jinhua Liu\",\"doi\":\"10.1109/ICIVC.2018.8492868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the increasing demands of copyright protection, digital watermarking has been paid more and more attention. In the design of a watermarking method, the modeling of signal by a general parametric family of statistical distributions plays an important role in many image watermarking applications. In this paper, the probability density function of wavelet coefficients is modeled by the generalized Gaussian distribution (GGD), and the decision threshold is obtained by the Neyman-Pearson (NP) criterion. In the procedure of watermark embedding, the energy of image block is considered in the watermark embedding. Only those blocks whose energy exceeds a predetermined threshold are used to embed the watermark data. Its improved robustness is due to embedding in the significant wavelet coefficients based on the energy scheme and control of its strength factor from the variance of coefficient. Experimental results demonstrate that the effectiveness of the presented watermarking and its robustness against common image processing and some kinds of geometric attacks.\",\"PeriodicalId\":173981,\"journal\":{\"name\":\"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIVC.2018.8492868\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC.2018.8492868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Image Watermarking Algorithm Based on Energy Scheme in the Wavelet Transform Domain
With the increasing demands of copyright protection, digital watermarking has been paid more and more attention. In the design of a watermarking method, the modeling of signal by a general parametric family of statistical distributions plays an important role in many image watermarking applications. In this paper, the probability density function of wavelet coefficients is modeled by the generalized Gaussian distribution (GGD), and the decision threshold is obtained by the Neyman-Pearson (NP) criterion. In the procedure of watermark embedding, the energy of image block is considered in the watermark embedding. Only those blocks whose energy exceeds a predetermined threshold are used to embed the watermark data. Its improved robustness is due to embedding in the significant wavelet coefficients based on the energy scheme and control of its strength factor from the variance of coefficient. Experimental results demonstrate that the effectiveness of the presented watermarking and its robustness against common image processing and some kinds of geometric attacks.