ROBUST LOSSLESS WATERMARKING USING ALPHA-TRIMMED MEAN AND SVM

摘要：

This paper presents a robust lossless watermarking technique using a-trimmed mean and support vector machine (SVM), which is called the RLW method hereafter. It does not damage the contents of original images during watermark embedding, because it uses trained SVMs to memorize the watermark or owner signature and then exploits the trained SVMs to estimate the watermark. Meanwhile, its robustness can be enhanced using a-trimmed mean operator against attacks. Experimental results demonstrate that the RLW method not only possesses the robust ability to resist on image-manipulation attacks under consideration but also, in average, is superior to other existing methods being considered in the paper.

关键词： Image Authentication lossless image watermarking support vector machine a-trimmed mean

作者: HUNG-HSU TSAI HOU-CHIANG TSEZG YEN-SHOU LAI

作者单位: Department of Information Management, National Formosa University, Huwei, Yunlin, 632 Taiwan Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi,

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3347-3353

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）