文章摘要
杨金劳,付利军,张福泉.基于椭圆特征区域与重要位平面分解的鲁棒图像水印算法[J].包装工程,2018,39(21):206-215.
YANG Jin-lao,FU Li-jun,ZHANG Fu-quan.Robust Image Watermarking Algorithm Based on Elliptical Feature Region and Important Bit-Plane Decomposition.Packaging Engineering,2018,39(21):206-215.
基于椭圆特征区域与重要位平面分解的鲁棒图像水印算法
Robust Image Watermarking Algorithm Based on Elliptical Feature Region and Important Bit-Plane Decomposition
投稿时间:2018-05-05  修订日期:2018-11-10
DOI:10.19554/j.cnki.1001-3563.2018.21.036
中文关键词: 图像水印  颜色不变性  局部椭圆特征区域  位平面分解  概率密度梯度  Hessian矩阵  直方图
英文关键词: image watermarking  color invariance  local elliptical feature region  bit-plane decomposition  probability density gradient  Hessian matrix  histogram
基金项目:国家教育部博士点基金(20121101110037);山西省自然科学基金(2013011121-1)
作者单位
杨金劳 1.山西运城农业职业技术学院 信息技术系运城 044000 
付利军 1.山西运城农业职业技术学院 信息技术系运城 044000 
张福泉 2.北京理工大学 软件学院北京 100081 
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全文下载次数: 45
中文摘要:
      目的 为了解决当前基于特征点的水印方案难以描述图像的非纹理区域(像素强度变化较大的边缘、像素强度值较小或趋于0的均匀区域),降低了局部特征区域的鲁棒性,使其抗几何失真能力不佳的问题,提出基于椭圆特征区域与重要位平面分解的鲁棒图像水印算法。方法 根据彩色载体的RGB分量,计算颜色不变性;基于颜色不变性的强度概率密度,推导概率密度梯度估计函数;利用概率密度梯度值,计算二阶Hessian矩阵,改进SURF方法,充分提取彩色载体中纹理与非纹理区域的鲁棒特征点;再求取Hessian矩阵的特征值与特征向量,以计算椭圆的长轴、短轴半径与方向角度,并以特征点为中心,建立局部椭圆特征区域;定义鲁棒特征区域选择规则,确定合适的水印嵌入位置;引入位平面分解技术,获取鲁棒椭圆特征区域的重要位平面图像,并提取其直方图,以此设计水印嵌入方法,将二值水印隐藏到这些直方图中,形成水印图像;根据水印检测机制,复原二值水印。结果 实验结果显示,与基于特征点的水印方案相比,所提算法具有更高的不可感知性与鲁棒性,复原水印失真度最小。结论 所提算法具有较高的视觉隐秘性和抗几何失真能力,在版权保护、信息防伪等领域具有较好的参考价值。
英文摘要:
      The work aims to propose a robust image watermarking algorithm based on elliptical feature region and important bit-plane decomposition, for the purpose of solving the defects such as low robustness of the local feature region induced by difficultly in describing the non-texture region of the image (the edge with greater pixel intensity changes, uniform area where pixel intensity is smaller or tends to be zero) in current watermarking scheme based on feature points, thus resulting in its poor anti-geometric distortion ability. Firstly, the color invariance was calculated according to the RGB component of the color carrier. The probability density gradient estimation function was derived based on the intensity probability density of color invariance. Subsequently, the probability density gradient was used to compute the two-order Hessian matrix for improving the SURF method, which fully extracted the robust feature points of the texture and non-texture regions in the color image. The eigenvalues and eigenvectors of the Hessian matrix were obtained to calculate the radius of the long axis and short axis with the ellipse, as well as the angle of the direction; and the local elliptical feature region was established by taking the feature points as the center. Then, a robust feature region selection rule was defined to determine the appropriate watermark embedding location. The bit-plane decomposition technique was introduced to get the important bit-plane image of the robust elliptical feature region, and its histogram was extracted. Based on that, the watermark embedding method was designed to hide the binary watermark in these histograms for forming watermark image. Finally, the binary watermark was restored based on watermark detection mechanism. The experimental results showed that the proposed algorithm had higher uncertainty and robustness, as well as the least distortion of the restored watermark, compared with the current watermarking scheme based on feature points. The proposed algorithm has higher visual stealth and anti-geometric distortion ability, which has better reference value in copyright protection, information security and other fields.
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