一种基于JND模型的小波域图像盲水印算法

随着现代科学技术的进步,数字水印技术逐渐成为了人们研究的一个热点问题。本文旨在研究临界可见误差(Just Noticeable Difference,JND)模型的小波域图像盲水印算法,并根据人类视觉系统(Human Visual System,HVS)的视觉特性,对宿主图像进行了二阶小波变换,并提出了较为新颖的实验算法,对实验结果进行了较为充分的分析。实验结果表明,在满足数字水印不可察觉性的基础上,本算法对于一些常见的水印攻击,都可以保持良好的鲁棒性,是一种有效的盲水印算法。     

脊波变换域的一种自适应盲水印算法

为了克服水印鲁棒性和图像视觉质量之间的矛盾,提出了一种脊波变换域自适应盲水印算法.利用脊波变换对图像特征的稀疏表示特性,找出各区域块中视觉重要信息的位置,并将水印嵌入其中.算法中水印嵌入强度依据图像块的内容属性变化,采用了系数可容忍的最大误差作为水印嵌入强度因子,较好地解决了水印鲁棒性和图像视觉质量之间的矛盾.实验结果表明,该算法能够抵抗JPEG压缩、加噪和任意裁剪等攻击,具有很强的鲁棒性,是一种自适应盲水印算法.     

基于混沌映射的小波变换域数字水印算法

本文结合传统的密码学和小波变换域数字水印技术,在水印信息被嵌入到载体作品之前,根据密码学原理将水印进行加密,然后再进行嵌入,充分考虑了小波变换的频域特性与人类视觉系统的很好匹配.在前人的研究基础上,利用图像的小波变换域的JND模型提出了一种基于混沌映射的在图像的小波变换域嵌入数字水印的算法.实验证明,用该算法嵌入的水印...     

基于DWT域图像数字水印技术

提出了一种基于DWT域的自适应盲检测数字水印算法,将二值水印图像自适应地嵌入到经小波变换后的原始图像的逼近子图上。实验结果显示,不论是视觉主观还是客观计算上,此算法得到的水印图像都具有很好的不可见性,对常见图像攻击和处理后的水印检测情况证明此算法所得的水印图像具有很强的鲁棒性。     

一种基于小波分析的自适应图像水印算法

提出一种在小波变换域自适应嵌入数字水印的算法。由人眼视觉系统特性,在第三级小波系数子图中 嵌入水印信号。水印强度与图像的显著性成正比,使水印对原图像具有自适应性。对水印图像进行各种退化处理的 实验表明,提出的数字水印算法具有很好的鲁棒性。     

一种基于小波分析的自适应图像水印算法

提出一种在小波变换域自适应嵌入数字水印的算法。由人眼视觉系统特性，在第三级小波系数子图中嵌入水印信号。水印强度与图像的显著性成正比，使水印对原图像具有自适应性。对水印图像进行各种退化处理的实验表明，提出的数字水印算法具有很好的鲁棒性。     

基于小波变换和HVS的数字水印算法研究

以静止彩色图像为研究对象,结合人类视觉系统模型,提出了一种基于离散小波变换的数字水印算法,该算法选择在原始图像小波变换的低频子带嵌入水印,提高了水印的鲁棒性和不可见性,并对所提出的算法进行了性能分析,实验结果表明,该算法对常见的图像攻击具有较强的鲁棒性。     

一种基于HVS和DC分量的DCT域数字水印算法

为增强数字水印的透明性和鲁棒性,提出了一种基于人类视觉系统和直流分量的DCT变换域数字水印算法。算法依据原始图像的纹理掩蔽特性确定水印信息的嵌入位置,依据照度掩蔽特性确定水印信息的嵌入强度,将二值水印图像嵌入到原始图像在DCT域的直流分量中。在仿真实验中与同等条件下的中频嵌入做比较,实验结果表明,该算法提取的水印具有良好的透明性和鲁棒性。     

基于有限脊波变换的图像内容认证算法

在有限脊波变换(FRIT,finite ridgelet transform)域,提出了一种用于图像内容认证的半易损数字水印算法。提取图像在FRIT时产生的均值信息作为图像内容特征,并由它生成认证水印。利用Bayesian最小风险函数估计选择FRIT系数,把认证水印嵌入这些系数。实验结果表明,此算法简便高效,能很好地区分图像内容的恶意篡改和偶然修改,并能确切指出篡改的位置。     

一种基于余数的小波域二值图像水印算法

利用小波变换域中人眼的视觉特性，提出了一种提取水印时不需要原始图像的数字图像水印算法。给出了一种基于余数的水印嵌入公式，并研究了算法中的参数估计。仿真表明，这种算法实现简单，能较好地平衡数字水印不可见性和稳健性的要求。     

一种基于图像内容的小波域半脆弱水印算法

在充分考虑图像视觉屏蔽特性的基础上,提出一种小波域图像半脆弱水印算法.嵌入水印基于小波低频子带系数产生,该方案通过自适应地修改小波变换后水平与垂直细节子带系数以嵌入水印,嵌入水印具有较好的透明性.实验结果证明了算法对非恶意攻击的鲁棒性和对恶意攻击的脆弱性.     

Visual saliency’s modulatory effect on just noticeable distortion profile and its application in image watermarking

Perceptual watermarking should take full advantage of the results from human visual system (HVS) studies. Just noticeable distortion (JND) gives us a way to model the HVS accurately. In this paper, another very important aspect affecting human perception, visual saliency, is introduced to modulate JND model. Based on the visual saliency’s modulatory effect on JND model which incorporates visual attention’s influence on visual sensitivity, the saliency modulated JND profile guided image watermarking scheme is proposed. The saliency modulated JND profile guided watermarking scheme, where the visual sensitivity model combined with visual saliency’s modulatory effect is fully used to determine image-dependent upper bounds on watermark insertion, allows us to provide the maximum strength transparent watermark. Experimental results confirm the improved performance of our saliency modulated JND profile guided watermarking scheme in terms of transparency and robustness. Our watermarking scheme is capable of shaping lower injected-watermark energy onto more sensitive regions and higher energy onto the less perceptually significant regions in the image, which yields better visual quality of the watermarked image. At the same time, the proposed saliency modulated JND profile guided image watermarking scheme is more robust compared to unmodulated JND profile guided image watermarking scheme.     

基于BJND模型的立体图像鲁棒水印算法

随着3D技术的发展,3D电影逐渐进入到一个快速发展阶段.低感知强鲁棒的数字水印方案是水印技术在3D电影版权保护应用中的一项研究重点.已有的研究证明,基于HVS的JND模型在提高水印不可见性上具有很好的效果,但是现有的JND模型大都建立在单目视觉掩蔽特性基础上,而对于立体图像需要进一步考虑双目视觉掩蔽特性.提出了一种基于BJND(Binocular JND)模型的立体图像鲁棒水印算法.水印嵌入强度受BJND值调制后,使得水印表现出更好的透明性.实验结果证明,在水印不可见的条件下,基于BJND模型的立体水印方案在抵抗加噪、JPEG压缩等全局攻击上具有更强的鲁棒性.     

基于BCH和JND的小波域数字图像水印算法

为了提高数字图像水印的鲁棒性,提出一种结合BCH码和JND模型的小波域数字图像水印算法.该算法采用Ar-nold变换和BCH码对二值水印图像进行置乱加密以及编码,对载体图像分块后再进行二维离散小波变换,将加密并且编码后的水印信息嵌入到载体图像各子块的二维离散小波变换的低频子带中,嵌入强度根据载体图像的JND值自适应的选取.实验结果表明,水印的透明性很好,在遭受不同类型和强度的水印攻击时,具有较强的鲁棒性.     

A combined just noticeable distortion model�Cguided image watermarking

Perceptual watermarking should take full advantage of the results from human visual system (HVS) studies. Just noticeable distortion (JND), which refers to the maximum distortion that the HVS does not perceive, gives us a way to model the HVS accurately. In this paper, we exploit a combined JND model, which represents an additional, accurate, perceptual visibility threshold profile to guide watermarking for digital images. The combined JND model-guided watermarking scheme, where visual models are fully used to determine image-dependent upper bounds on watermark insertion, allows us to provide the maximum strength transparent watermark. Experimental results confirm the improved performance of our combined JND model. Our combined JND model is capable of yielding higher injected-watermark energy without introducing noticeable distortion to the original image and outperforms the relevant existing visual models. Simulation results show that the proposed JND model-guided image watermarking scheme is more robust than other algorithms based on the relevant existing perceptual models while retaining the watermark transparency. At the same time, the proposed combined JND model has much lower computational complexity compared with the relevant existing perceptual models.     

基于HVS和DWT的双彩色数字图像水印技术

根据HVS系统,人眼对于红、绿、蓝三色的敏感程度是不同的。人眼对于蓝色分量最不敏感,故嵌入水印有良好的透明性,但同时在红色分量中嵌入水印具有良好的鲁棒性。为解决水印嵌入的视觉失真问题,采用了不同基色对应不同的加权系数的水印嵌入方案。同时,将彩色水印图像嵌入彩色宿主图像的中频段以及多次嵌入低频段来增加算法的鲁棒性。仿真结果表明,该算法具有较好的透明性,且提取水印效果较好。     

Wavelet-based watermarking for color images through visual masking

In this paper, a wavelet-based watermarking scheme for color images is proposed. The watermarking scheme is based on the design of a color visual model that is the modification of a perceptual model used in the image coding of gray scale images. The model is to estimate the noise detection threshold of each wavelet coefficient in luminance and chrominance components of color images in order to satisfy transparency and robustness required by the color image watermarking technique. The noise detection thresholds of coefficients in each color component are derived in a locally adaptive fashion based on the wavelet decomposition, by which perceptually significant coefficients are selected and a perceptually lossless quantization matrix is constructed for embedding watermarks. Performance in terms of robustness and transparency is obtained by embedding the maximum strength watermark while maintaining the perceptually lossless quality of the watermarked color image. Simulation results show that the proposed scheme is more robust than the existing scheme while retaining the watermark transparency.     

Image watermarking using tree-based spatial-frequency feature of wavelet transform

In this paper, an image watermarking scheme is developed using the tree-based spatial-frequency feature of wavelet transform. With our approach, the watermark sequence is inserted in those high activity texture regions of an image having the maximum strength of just noticeable distortion (JND) tolerance of the human visual system (HVS). Simulation results show that the proposed method achieves a good compromise between the robustness and transparency.     

Perceptual watermarking using a new Just-Noticeable-Difference model

In this paper, a new watermarking scheme based on Human Visual System (HVS) modeling is proposed. The approach consists in building computational models which take into account the most common properties of the HVS that can be exploited for watermarking. Two schemes for embedding and controlling the transparency of the watermark are presented, namely the implicit and the explicit schemes. Both schemes are designed in the framework of the pyramidal decomposition which has been shown to be a powerful tool for analyzing image through a multi-scale representation. For the first approach, a multi-scale visibility map is used to optimize the watermark embedding process. The second approach makes use of HVS properties in an explicit and more sophisticated manner that consists in tuning the watermark strength just beneath the visual detection threshold. A new JND (Just-Noticeable-Difference) model for determining this threshold is then proposed and evaluated. The obtained results provide a strong support for this new JND model.