SPREAD SPECTRUM WATERMARK DETECTION IN DRT-DOMAIN  

作　　者：Xing Guihua Yu Shenglin 

机构地区：[1]Department of Automatic Engineering, Nanjing University Aeronautics and Astronautics, Nanjing 210015, China

出　　处：《Journal of Electronics(China)》2007年第6期782-786,共5页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No.10371055).

摘　　要：The traditional correlation-based detector is optimal only for Gaussian data, but the Laplacian Probability Density Function (PDF) is more appropriate to model the coefficients in the Discrete Ridgelet Transform (DRT) domain. An additive maximum-likelihood detector based on the Laplacian PDF is analyzed and the theoretical result of its performance is given. The experiments show that the error of the Laplacian model for the DRT coefficients of many images is smaller than that of the Gaussian model. The experiments also prove that the Laplacian detector is superior to the tradi- tional correlation-based detector.The traditional correlation-based detector is optimal only for Gaussian data, but the Laplacian Probability Density Function （PDF） is more appropriate to model the coefficients in the Discrete Ridgelet Transform （DRT） domain. An additive maximum-likelihood detector based on the Laplacian PDF is analyzed and the theoretical result of its performance is given. The experiments show that the error of the Laplacian model for the DRT coefficients of many images is smaller than that of the Gaussian model. The experiments also prove that the Laplacian detector is superior to the traditional correlation-based detector.

关 键 词：Ridgelet transform Digital watermarking Laplacian model Statistical detection 

分 类 号：TN912.3[电子电信—通信与信息系统]