基于Gauss-Hermite分布模型的水印检测算法研究

图像水印的检测来源于统计信号处理的思想,其关键在于如何对图像经变换后的系数进行建模。在已有的水印检测模型中,图像基于高斯分布进行建模,容易导致在抗攻击条件下水印检测的性能较低。为克服这一不足,结合图像的视觉感知模型和高斯-埃尔米特(Gauss-Hermite)统计分布模型,提出一种数字水印的嵌入和盲检测方法。在水印嵌入中,以临界可见误差值JND(Just Noticeable Difference)作为水印嵌入强度,以提高水印的不可感知性。在水印检测中,基于高斯-埃尔米特分布对图像小波系数进行建模,并通过假设检验提出了水印的盲检测模型,最后推导了虚警概率和检测概率之间的工作特性关系。仿真结果表明:提出的水印检测模型在诸如噪声、JPEG压缩、滤波以及缩放等攻击下具有较好的性能。     

抗几何攻击图像水印算法研究

抗几何攻击的图像水印算法作为多媒体水印研究领域的一个热点扣难点问题受到关注.研究了几何攻击类型的估计方法,提出了一种基于边信息和仿射模型的抗几何攻击小波域自适应图像水印算法.该算法根据HVS的掩蔽特性将水印信息自适应地嵌入到图像小波域的低频子带中,在保证不可见性的前提条件下,根据几何攻击估计方法来判断图像所经受的几何攻击类型.实验结果表明,提出的几何攻击估计方法简单、有效,该水印算法不仅具有较好的不可感知性,而且对诸如平移.旋转、缩放、镜像和转置等几何攻击以及一般的图像处理均具有较强的鲁棒性.     

基于DWT及方向可控金字塔变换的抗几何攻击水印

几何攻击会使水印嵌入与检测失去同步,影响水印的检测。为解决几何攻击造成的水印同步问题,基于方向可控金字塔变换域特征实现了水印图像旋转失真的检测,结合归一化小波树熵掩码及小波域人眼对比敏感度掩码完成了小波超树的自适应调制,实现了水印信息的嵌入,基于小波超树算法的抗缩放攻击能力以及采用方向可控金字塔变换设计,算法能够抵抗缩放攻击和旋转攻击。仿真结果表明,算法对几何攻击具有较强的鲁棒性。     

基于神经网络的小波域数字水印嵌入算法研究

提出一种结合神经网络将二值水印嵌入离散小波变换后的宿主图像中的新方法.为使算法具有更好的不可感知性和鲁棒性,进一步提高它的实用性,结合神经网络理论,创新地提出在小波域实现对数字水印嵌入.该方法是对宿主图像做离散小波分解,取分解后的近似分量作为嵌入位置.在其中随机的选取一些像素点及其邻域,利用神经网络对其进行建模及训练,通过修改其像素值嵌入水印信息.在嵌入之前对二值水印进行了Arnold变换来加密.实验结果表明,算法具有很强的抗几何攻击和承受其他图像处理操作的能力,不可感知性好,鲁棒性明显优于一般小波域嵌入算法,对数字水印的实现具有很强的参考价值.     

基于图像纹理复杂度的数字水印算法

针对变换域数字水印算法中水印信息鲁棒性和不可感知性之间的矛盾,提出一种基于人类视觉系统特征的离散余弦变换(DCT)数字水印算法。对水印信息进行Arnold置乱变换,将水印信息量化嵌入到载体图像的DCT域直流分量中。实验结果表明,该算法能有效抵抗噪音干扰、裁剪和压缩编码等攻击,具有较好的鲁棒性。     

基于小波变换的图像鲁棒水印算法

分析了水印嵌入中的几个关键问题,确定了问题的解决方法,提出一种小波域透明图像水印算法,利用图像经小波分解后小波系数的特性,并利用人眼视觉掩蔽特性对水印的添加强度进行自适应调节,以解决水印的鲁棒性和不可感知性的矛盾问题。     

一种基于小波包分解的自适应数字水印嵌入算法

提出一种新颖的基于小波包分解的自适应数字水印算法.为了提高水印图像的攻击鲁棒性和水印的不可见性,该算法提出基于人类视觉特性(HVS)和小波包分解的数字水印算法,同时引入信噪比自适应水印嵌入机制.实验表明应用该算法对宿主图像嵌入水印可以达到更好的抗攻击鲁棒性和视觉效果.     

一种心理声学模型的自同步音频水印方案

提出了一种鲁棒的数字音频水印方案,该方案主要特点：（1）应用双重置乱机制,保证水印的安全性;（2）利用心理声学模型算法,确定水印嵌入强度;（3）应用同步机制,实现水印的自同步检测;（4）在离散小波变换（DWT）域嵌入水印,提高水印的抗攻击能力;（5）利用高效的嵌入算法,提高水印的检测效率。仿真实验表明,该方案不但具有良好的不可感知性,还对诸如重采样、重量化、叠加噪声、低通滤波、MP3压缩等攻击具有较好的鲁棒性。     

基于小波包分解的自适应数字水印嵌入算法

提出一种新颖的基于小波包分解的自适应数字水印算法。为了提高水印图像的攻击鲁棒性和水印的不可见性,该文提出基于人类视觉特性(HVS)和小波包分解的数字水印算法,同时引入信噪比自适应水印嵌入机制。实验表明:应用该算法对宿主图像嵌入水印可以达到更好的抗攻击鲁棒性和视觉效果。     

基于模糊聚类的小波域数字图像水印算法*

结合人眼视觉系统的感知特性与图像的局部相关特性,提出了一种基于模糊聚类的小波域数字图像水印嵌入方案.该方案首先对原始载体图像实施小波变换;然后结合视觉感知特性,在小波域内进行模糊C-均值(FCM)聚类分析,自适应确定出数字水印的嵌入位置与嵌入强度;最后利用图像自身的局部相关性,通过修改小波系数值,将数字水印嵌入到宿主图像内.该方案在提取数字水印信息时,不需要原始载体图像.实验结果表明,该自适应小波域图像水印嵌入技术不仅具有较好的透明性,而且对诸如叠加噪声、JPEG压缩、平滑滤波、几何剪切、图像增强、马赛克效果等攻击均具有较好的鲁棒性.     

一种基于多尺度空间的图像水印算法

当前数字水印对常见的图像处理攻击如高斯噪声、中值滤波、有损压缩等具有较强的抵抗能力,但其抵抗剪切、缩放等几何攻击的能力较差。因此,为提高数字水印的鲁棒性,结合尺度不变特征变换算子和视觉感知模型,提出一种基于不变特征的水印嵌入方法。基于广义高斯分布模型对小波系数进行刻画,并根据二值假设检验理论确定水印的检测阈值,由此进一步给出水印虚警和漏警之间的工作特性曲线关系。实验仿真结果表明提出的水印方法在抗压缩、叠加噪声、缩放、剪切、旋转等攻击时具有较好的鲁棒性能。     

An adaptive color image watermarking using RDWT-SVD and artificial bee colony based quality metric strength factor optimization

Image watermarking has emerged as a useful method for solving security issues like authenticity, copyright protection and rightful ownership of digital data. Existing watermarking schemes use either a binary or grayscale image as a watermark. This paper proposes a new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension. The security of the proposed watermarking scheme is enhanced by scrambling both color host and watermark images using Arnold chaotic map. The host image is decomposed by redundant discrete wavelet transform (RDWT) into four sub-bands of the same dimension, and then approximate sub-band undergoes singular value decomposition (SVD) to obtain the principal component (PC). The scrambled watermark is then directly inserted into a principal component of scrambled host image, using an artificial bee colony optimized adaptive multi-scaling factor, obtained by considering both the host and watermark image perceptual quality to overcome the tradeoff between imperceptibility and robustness of the watermarked image. The hybridization of RDWT-SVD provides an advantage of no shift-invariant to achieve higher embedding capacity in the host image and preserving the imperceptibility and robustness by exploiting SVD properties. To measure the imperceptibility and robustness of the proposed scheme, both qualitative and quantitative evaluation parameters like peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and normalized cross-correlation (NC) are used. Experiments are performed against several image processing attacks and the results are analyzed and compared with other related existing watermarking schemes which clearly depict the usefulness of the proposed scheme. At the same time, the proposed scheme overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based watermarking schemes.     

水印能量估计的一般性框架

数字图像水印技术为网络环境下多媒体数据的传输提供了一个有效的版权保护方案 ,当前大多数的水印算法均利用实验来确定嵌入的水印的最佳能量。该文提出了一个一般性的水印算法框架,可以从理论上计算需嵌入水印的最佳容量或能量,对基于酉变换域方法的数字水印算法而言,该框架可以作为一个有用的指导方法。     

Robust gray-scale logo watermarking in wavelet domain

In this paper, a robust logo watermarking scheme based on image fusion is proposed. Unlike existing watermarking schemes, the used watermark is a gray scale logo instead of randomly generated Gaussian noise type watermark. The core idea of the proposed scheme is to decompose an image into frequency sub-bands using wavelet transform followed by the embedding in selected blocks of sub-bands obtained by ZIG-ZAG sequence. Block selection is done by taking variance of the blocks into consideration. The experimental results show better visual imperceptibility and resiliency of the proposed scheme against intentional or un-intentional variety of attacks and superiority is carried out by comparison made by us with the existing schemes.     

基于神经模糊控制的自适应图像水印算法

提出了基于神经模糊控制网络的新的自适应图像水印算法。该算法首先对原始图像进行小波变换,组织小波块。接着,从子块的视觉掩蔽性和能量强度两个方面,建立和训练神经模糊控制网络,得到每个小波子块嵌入水印的最优强度值。最后,根据神经模糊控制得出的最优嵌入强度,将不同强度的水印嵌入到不同的小波块中。实验结果表明,所提出的算法能抵抗各种常规的图像处理的攻击,具有较好的鲁棒性和透明性。     

Robust Scaling-Based Image Watermarking Using Maximum-Likelihood Decoder With Optimum Strength Factor

In this paper, a new scaling-based image-adaptive watermarking system has been presented, which exploits human visual model for adapting the watermark data to local properties of the host image. Its improved robustness is due to embedding in the low-frequency wavelet coefficients and optimal control of its strength factor from HVS point of view. Maximum likelihood (ML) decoder is used aided by the channel side information. The performance of the proposed scheme is analytically calculated and verified by simulation. Experimental results confirm the imperceptibility of the proposed method and its higher robustness against attacks compared to alternative watermarking methods in the literature.     

RDWT domain statistical watermark detector using FRHFMs magnitudes and bivariate Cauchy-Rayleigh distribution

Imperceptibility, robustness and data payload, which are complimentary to each other, are widely considered as the three main properties vital for any image watermarking systems. It is a challenging work to design a statistical model-based multiplicative watermarking scheme for achieving the tradeoff among three main properties. In this paper, we propose a novel statistical image watermarking scheme by modeling local redundant discrete wavelet transform (RDWT) and fast Radial harmonic Fourier moments (FRHFMs) magnitudes with bivariate Cauchy-Rayleigh distribution. Our image watermarking scheme consists of two parts, namely, embedding and detection. In the embedding process, RDWT is firstly performed on the host image and RDWT highpass subbands are divided into non-overlapping blocks. Then FRHFMs are computed on RDWT coefficient blocks. And finally, the watermark signal is inserted into robust RDWT-FRHFMs magnitudes through a non-linear multiplicative approach. In the detection process, robust local RDWT-FRHFMs magnitudes are firstly modeled by employing bivariate Cauchy-Rayleigh distribution, which can capture accurately both marginal distributions and strong dependencies of local RDWT-FRHFMs magnitudes. Statistical model parameters are then estimated effectively by the method of logarithmic cumulants (MoLC) approach. And finally, an image watermark detector for multiplicative watermarking is developed using bivariate Cauchy-Rayleigh model and locally most powerful (LMP) test. Also, we utilize the bivariate Cauchy-Rayleigh model to derive the closed-form expressions for the watermark detector. After performance testing and comparison with the experimental results of existing methods, the proposed statistical image watermarking method has achieved relatively ideal results in terms of robustness, imperceptibility and data payload.

     

图像盲水印算法的遗传优化

为了提高数字水印的实用性,必须保证水印的鲁棒性和透明性,为此在Cox等人提出的水印稳健性理论的指导下,充分考虑人类视觉系统特性,提出了一种基于遗传算法的图像盲水印算法。通过基于小波边缘的检测,调节系数差,实现了水印透明性和鲁棒性的综合优化。实验结果表明,该算法不仅对于水印的攻击具有强鲁棒性,且兼顾载体图像的质量。     

基于支持向量机的NSCT域自适应图像水印算法

为进一步提升支持向量机水印算法鲁棒性,提出基于支持向量机的NSCT域自适应图像水印算法。主要思想是根据图像自身特征生成自适应嵌入水印序列,利用模糊核聚类和支持向量机对NSCT低频系数进行分类,选取适合嵌入水印的低频系数,然后利用支持向量机建立NSCT邻域系数的关系模型,自适应完成水印嵌入。算法具有良好的不可感知性、安全性,并通过嵌入自适应水印达到全盲水印检测。实验结果表明,提出算法对高斯噪声、椒盐噪声、低通滤波、中值滤波、均值滤波、JPEG、旋转、平移和尺寸缩放有很强的鲁棒性。