基于特征点密度的DCT域盲检测数字水印算法  

Blind detection digital watermarking algorithm in DCT domain based on density of feature point

作　　者：曲巨宝[1] 林宏基[2] 

机构地区：[1]武夷学院数学与计算机系,福建武夷山354300 [2]福州大学数学与计算机科学学院,福州350002

出　　处：《计算机应用》2011年第10期2670-2673,2696,共5页journal of Computer Applications

基　　金：福建省自然科学基金资助项目(2006J0414)

摘　　要：针对数字水印图像遭受几何攻击问题,提出了一种融合图像不变特征和频域稳定特性的强鲁棒数字水印盲检测算法。通过构造自适应尺度不变特征变换(SIFT)算法和Harris角点补位法,利用在不同尺度空间获取的特征点的密度自适应地调整水印信息嵌入到离散余弦变换(DCT)域的强度;将图像子块的特征向量集进行Arnold置乱,生成密钥文件,与隐秘图像的特征向量做双向特征匹配,获得几何失真参数并进行图像恢复性校正,以盲检测的形式提取IDCT反变换域水印信息。从实验结果来看,该算法比使用离散小波变换(DWT)和离散傅里叶变换(DFT)的峰值信噪提高13%,水印相似度提高11%,说明该文算法在获得较好的不可见性的同时,对几何攻击和常规信号处理均具有良好的鲁棒性。To solve the problem that the digital watermarking images suffer geometric attacks,a strong robust blind detection digital watermarking algorithm,integrated with the unchanged features of the image and the characteristics of the frequency-domain stability,was proposed.By constructing the self-adaptive Scale Invariant Feature Transform（SIFT） algorithm and filling the seats with the Harris method of the angle and the point,utilizing the density of feature points obtained in different scale space and adjusting the watermarking information self-adaptively to the intensity embedded into Discrete Cosine Transform（DCT） domain,it scrambled the image sub-block feature vector set with Arnold scrambling,and then generated the key documents,next matched the two-way feature with the secret image feature vector,got geometric distortion parameters and corrected the image restoratively,and extracted the IDCT inverse transform domain watermark information in the form of blind detection.Judging from the experimental results,the algorithmic Peak Signal to Noise Ratio（PSNR） in this paper improves by 13% than using Discrete Wavelet Transform（DWT） and Discrete Fourier Transform（DFT）,and the similarity of watermark is over 11%.It shows that this algorithm has good robustness to both of the geometric attacks and the conventional signal processing,as well as better invisibility.

关 键 词：特征点密度 数字水印 几何攻击 离散余弦变换 盲检测 

分 类 号：TP309.7[自动化与计算机技术—计算机系统结构] TN911.73[自动化与计算机技术—计算机科学与技术]