图像块的自适应均衡水印算法  

Watermarking algorithm for adaptive equalization based on image block

作　　者：齐向明[1] 李玥 高婷[1] 郑泽 Qi Xiangming, Li Yue, Gao Ting, Zheng Ze(College of Software, Liaoning Technical University, Huludao 125105, Chin)

机构地区：[1]辽宁工程技术大学软件学院,葫芦岛125105

出　　处：《中国图象图形学报》2018年第4期467-477,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61401185)~~

摘　　要：目的针对水印算法通常利用实验确定强度参数,实验工作量大并且具有随机性,得到的参数无法较好地均衡水印不可见性和鲁棒性,提出一种基于图像块的自适应均衡水印算法。方法利用尺度不变特征变换(SIFT)提取原始图像中鲁棒性强的特征点作为水印嵌入区域,将提取的嵌入区域分成4个大小相等且互不重叠的图像块,并对各图像块进行奇异值分解(SVD),得到与各块相应的奇异值矩阵,各块与水印做一级离散小波变换后产生的各子带相叠加,生成嵌入加密水印块,重组得到水印矩阵,降维后将特征点还原到原始图像。根据果蝇优化算法(FOA)中适应度函数迭代确定加密水印强度参数,构造水印图像的自适应嵌入,来均衡水印的不可见性和鲁棒性,水印检测可直接作用在受攻击后的图像上,无需校正恢复。结果对标准灰度图像进行多组实验,得到含水印图像峰值信噪比均达到43dB以上;对水印载体图像分别进行噪声、压缩、剪切、旋转仿真攻击实验,提取水印图像与原始水印图像的归一化相关系数都达到0.94以上。结论 SIFT算法实现图像块局部嵌入,提取特征点稳定性强,结合SVD算法使水印嵌入性能良好,利用FOA算法自适应确定最优参数,使水印图像嵌入效果达到最佳状态,最终均衡了水印的不可见性和鲁棒性。Objective Watermarking algorithm experiments are usually used as a standard to determine intensity parameters. However, the experimental workload is considerable and stochastic. A watermark is embedded in the original image to coordinate invisibility and robustness. The watermarked image has good invisibility but reduces the watermark robustness. A good watermark hiding technology must consider both the invisibility of the watermarked image and the robustness of the watermark and can resist various common attacks at the same time. An adaptive watermarking algorithm based on image block is proposed to balance invisibility and robustness. Method Scale-invariant feature transform （SIFT） is utilized to extract the original image with a robustness feature as good that of the watermark-embedded area. Before the embedding operation, feature points are filtered to eliminate the points with low contrast and edge point, and the resulting feature points have high stability. Two watermarking experiments with different watermark sizes are conducted. The extraction of the number of feature points is positively correlated with watermark size. When the watermark is extracted, the points are used again to locate the watermark. The feature points of the original carrier image are extracted to form a matrix with the same size as that of the watermarked image. The extracted embedded region is divided into four equal and non-overlapping image blocks. Each image block is decomposed by singular value decomposition （SVD） to obtain two orthogonal matrices U and V and a diagonal matrix S. Matrix S is superimposed by the subband of the first-class discrete wavelet transform of the watermark, which is embedded in the encrypted watermark. The watermark matrix is reorganized, and then the feature points are restored to the original image. The size of the embedded strength affects the perfornlance of the extracted watermark. If the embedding intensity is defined as a constant, then it cannot be applied to each experiment type. Random v

关 键 词：自适应水印 尺度不变特征变换 奇异值分解 离散小波变换 果蝇优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]