支持向量机与奇异值分解的盲水印算法  

Blind Watermarking Scheme Based on Support Vector Machine and Singular Value Decomposition

作　　者：王娟[1] 林耀进[1,2] 王育齐[1,3] 

机构地区：[1]闽南师范大学计算机学院,漳州363000 [2]合肥工业大学计算机与信息学院,合肥230009 [3]电子科技大学计算机科学与工程学院,成都611731

出　　处：《计算机科学》2014年第11期212-215,共4页Computer Science

基　　金：国家自然科学基金项目(61303131);福建省教育厅科技A类项目(JA13196);闽南师范大学科学研究资助项目(SK09002)资助

摘　　要：为进一步提高水印算法的抗攻击性能,提出了基于支持向量机(Support Vector Machine,SVM)与奇异值分解(Singular Value Decomposition,SVD)的盲水印算法。首先对宿主图像进行DWT变换,将低频子带分成互不重叠的子块;然后利用SVM建立子块的局部相关性模型,根据模型预测结果与对应位置的低频系数值的大小关系产生特征序列,该序列与水印进行异或运算产生特征水印序列,将特征水印序列通过奇偶量化规则嵌入原始图像小波低频子带对应子块的最大奇异值。实验结果表明,该算法不仅具有较好的不可感知性,而且具有较强的抗攻击能力。An image watermarking scheme was developed based on SVM（support vector machine）and SVD（singular value decomposition）,which is used to further enhance the performance against attacks.Firstly,the host image is decomposed by the DWT transform,and its low frequency wavelet band is split into non-overlapping blocks.Then,the partial correlation model of the block is established by using the support vector machine.A feature sequence is derived through judging the numerical relationship between the prediction results and the corresponding position of the low frequency coefficient values,and the feature watermark sequence is derived through exclusive-or the feature sequence and the watermark.Moreover,the feature watermark sequence is embedded into corresponding block’s biggest singular value from the original image’s low frequency wavelet band based on the principle of odd-even quantization.Finally,a watermarked image is obtained after SVD synthesis and IDWT.Experimental results show that the scheme is not only invisible but also has strong ability to resist attacks.

关 键 词：数字水印 支持向量机 奇异值分解 奇偶量化 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]