利用HVS特性与混沌映射的图像水印算法

提出一种基于混沌序列的图像空域水印算法。首先通过亮度、边缘、纹理特性计算出每象素视觉因子;然后由视觉因子和混沌映射确定每象素水印嵌入比特位;最后通过替换方式嵌入水印。水印提取时不需原始图像的参与,实验表明算法具有较好的水印透明性,并对一般信号处理攻击是鲁棒的。     

数字水印的透明性的灰评判

鲁棒性和透明性是数字水印的两大基本特征,关于水印鲁棒性的检测一直受到人们的普遍重视,但水印透明性却没有定量的检测指标。论文将灰色系统理论中的灰关联概念首次引入水印技术中,提出了一种适用于评价水印透明性的算法。该算法把载体信号转化为参考序列,把加入水印后的载体信号转化为比较序列,通过灰色关联度的整体比较机制来对水印的透明度做出客观的定量的评价。实验证明,这种基于灰关联的评判结果与HVS判断结果一致,能够用来评判水印透明度。     

不可见水印的透明性评价

为避免人眼评价不可见水印透明性时受疲劳、环境等因素的影响,提出一种与人类视觉感知一致的透明性评价方法.对原始图像和各含水印图像进行小波变换,按照人眼感知的敏感性对所得子图进行分类,形成相应的参考序列和比较序列,利用灰色关联分析理论计算原始图像与含水印图像对应各类序列之间的关联度,并根据水印嵌入面积比例和嵌入强度进行调整,将各类子图关联度加权,得到原始图像与各含水印图像的整体关联度,给出水印透明性的客观评价.实验结果表明,与PSNR等现有评价方法相比,该方法不仅正确有效、通用性好,而且细节区分能力强.     

一种小波域数字水印算法及其MATLAB仿真

该文提出了一种基于DWT域有意义二值图像数字水印的新算法。首先,利用Arnold变换对水印序列进行置乱,增强嵌入水印的安全性;再分别对原始图像和置乱后的水印进行离散小波变换,最后,在原始图像的中频子带图像中进行水印小波域的融合。试验结果表明,该算法嵌入的水印具有比较好的透明性和鲁棒性。     

一种基于小波变换和变换域系数值比较的数字水印算法

提出了一种基于小波变换和变换域系数值比较的盲检测数字水印算法.算法利用小波变换,将载体图像进行多层小波变换分解,找出合适的变换域系数,将该系数分解为数据对序列,使二值水印图像每个像素的值与该数据对序列中的每个数据对一一对应.用数据对内前后数据值的大小关系来表示0和1,若前者大于后者,则表示1;若后者大于前者,则表示0.然后调整数据对序列中每个数据对内数据的前后位置,使其和二值水印图像相应位置上的像素值匹配.这样就嵌入了水印.提取水印过程刚好和嵌入水印过程相反.实验证明,水印具有透明性,对高斯低通滤波、图像变亮、图像变暗、直方图均衡化、增加图像对比度、降低图像对比度、添加高斯噪声、添加椒盐噪声、添加乘积性噪声、JPEG压缩等图像处理和噪声干扰都具有很强的鲁棒性.该算法鲁棒性和透明性好,是一种可行的水印方案.     

基于低频奇异值均值的强鲁棒零水印算法

为增强水印算法的鲁棒性并兼顾其虚警率和透明性,提出一种基于低频奇异值均值的零水印算法。利用离散小波变换获得载体图像的稳定低频区域,采用离散剪切波变换提取其中较优尺度的低频分量,从而构造稳定特征区域。对该区域做分块离散余弦变换并计算奇异值矩阵,通过比较分块与整体的奇异值均值大小构建特征矩阵,并将其与水印图像进行异或运算构造零水印。构造水印时采用Arnold加密提高水印图像安全性,提取水印前则利用Radon变换校正载体图像加强算法鲁棒性。对不同纹理图像进行压缩、噪声、滤波和几何攻击,实验结果表明,在保证低虚警率和良好透明性的基础上,该算法具有较强的鲁棒性,所得到的归一化互相关函数值均高于0.98,部分可达到1.00。     

支持向量机与奇异值分解的盲水印算法

为进一步提高水印算法的抗攻击性能,提出了基于支持向量机(Support Vector Machine,SVM)与奇异值分解(Singular Value Decomposition,SVD)的盲水印算法。首先对宿主图像进行DWT变换,将低频子带分成互不重叠的子块;然后利用SVM建立子块的局部相关性模型,根据模型预测结果与对应位置的低频系数值的大小关系产生特征序列,该序列与水印进行异或运算产生特征水印序列,将特征水印序列通过奇偶量化规则嵌入原始图像小波低频子带对应子块的最大奇异值。实验结果表明,该算法不仅具有较好的不可感知性,而且具有较强的抗攻击能力。     

硬C-means聚类和DT-CWT变换的数字图像水印算法

为了提高数字水印图像的鲁棒性,提出一种基于硬C均值聚类和双树复小波变换域的图像水印算法。该算法对载体图像进行一层双树复小波变换分解,利用人类视觉特性对其2个低频子带进行硬C均值聚类划分,确定可嵌入信息区域。将二值水印图像信号经过Hilbert曲线置乱和降维,形成一维信号序列。利用图像自身局部相关性,调节水印嵌入强度并修改小波系数值,实现将水印嵌入到可嵌入信息区域。实验表明,该算法具有良好的透明性且对压缩、剪切、噪声和滤波等几何攻击具有高鲁棒性。     

基于扩频调制和视觉掩蔽的彩色图像盲水印算法

为了更好地实现彩色图像的版权保护,首先,从理论上说明了应该选择彩色图像绿色分量嵌入水印,然后提出了一种新的适应彩色图像的盲水印算法。该算法先对宿主图像的绿色分量以8×8pixels分块进行DCT变换;然后将二值水印图像用两个互不相关的伪随机序列进行扩频调制;最后为了最大限度地增强水印强度,且不影响透明性,通过引入人眼视觉掩蔽因子,将调制好的水印根据视觉掩蔽因子大小,不同强度地嵌入到DCT变换域的中频子带系数上。该算法是通过比较两个伪随机序列和水印化图像的相关性大小来提取水印,不需要原始图像的参与,为盲水印算法。实验结果证明,该算法能有效抵抗JPEG压缩、加噪、剪切、滤波等常见攻击,而且在绿色分量中嵌入水印比在红色和蓝色分量中嵌入水印能更好地抵抗JPEG压缩的攻击。     

基于面包师变换的抗剪切扩频水印算法研究

为提高扩频水印的抗图像剪切能力,提出一种基于面包师的扩频水印算法。该算法首先利用面包师变换对水印图像进行置乱;再通过一组伪随机序列对置乱后的图像进行扩频,可得到具有抗剪切攻击能力的水印序列;最后将水印序列嵌入到载体图像DCT域中低频段部分,完成水印的嵌入。水印检测时,对载体图像分别进行不同面积的剪切,计算出提取的水印图像与原始水印图像的相关值。实验结果证明,该算法不仅能够抵抗大面积的图像剪切,同时对其他攻击方法也能够表现出良好的鲁棒性。     

基于AVC和小波变换的数字图像水印嵌入量的研究

鲁棒性、不可见性和水印嵌入量是评价图像数字水印质量的3个重要指标，而水印嵌入量又是嵌入算法质量好坏的关键，在分析利用传统通信信道计算水印嵌入量的缺陷和不足的基础上，提出了一种新的计算水印嵌入量算法．该算法不仅分析了图像数字水印的特性，结合了视觉模型和小波变换的特点，而且利用了可变信道（AVC）的原理。实验结果证明，该算法不仅水印嵌入量大，而且水印的鲁棒性和不见性也相当高，此算法的出现为进一步实现信息的隐藏奠定了理论基础。     

A semi-blind HVS based image watermarking scheme using elliptic curve cryptography

In the present paper, an advanced encryption technique commonly known as Elliptic Curve Cryptography (ECC) is used to embed a binary image as a watermark in five grayscale host images in a semi-blind manner. The ECC algorithm is a fast encryption technique which successfully encrypts the subject with significantly less number of bits as compared to other popular encryption algorithms such as Rivest-Shamir-Adleman (RSA) and Direct Selling Association (DSA). In the proposed watermarking scheme, embedding in the grayscale host images is carried out in DWT-SVD domain. First, entropy based Human Visual System (HVS) parameters are computed block wise to identify the most appropriate blocks in spatial domain. First level DWT is computed for these selected blocks and watermark embedding is carried out by using the calculated Singular Value Decomposition (SVD) parameters. Preliminary results of this work show that proposed scheme outperforms the other similar schemes carried out in DCT-SVD domain without using any encryption method. It is concluded that the use of DWT-SVD hybrid architecture along with the fast encryption technique ECC is responsible for better performance in present case. In the second part of this simulation, an established HVS model working in DCT domain is implemented and compared with the entropy based HVS model implemented in transform domain to embed the ECC encrypted binary watermark in images. In this case also, proposed scheme performs better both in terms of visual imperceptibility and robustness as compared to other scheme. It is concluded that HVS parameters – Luminance, Contrast and Edge Sensitivity are better placed in comparison to entropy parameters to examine image features and characteristics for watermarking purpose.     

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.     

一种基于DFT的DEM自适应数字水印算法

通过对DEM数据进行分块傅里叶变换和频谱分析,结合人类视觉系统和DEM数据特征,自适应地确定水印嵌入位置,将水印信息以加性法则嵌入到傅里叶低频系数的幅值上,然后对DEM数据进行分块傅里叶逆变换,得到含水印信息的DEM数据。实验结果表明,该算法不仅具有很好的透明性,而且对DEM数据的精度影响较小,能够满足一定的应用要求,具有良好的抗噪能力。     

基于人类视觉系统的数字图像水印方法

基于人类视觉系统(HVS)提出一种可有效平衡数字水印鲁棒性和不可见性的方法。利用基于矩的图像归一化技术将载体图像映射到几何不变空间内,并结合不变质心理论在归一化图像中确定重要区域。根据人眼视觉系统掩蔽特性将一个大小为256 bit的可读水印自适应地嵌入到重要区域的离散傅里叶变换(DFT)域内,同时采用预失真补偿策略弥补图像归一化误差较大的缺陷。仿真实验结果表明,该算法不仅具有良好的透明性,而且对常规信号处理和大部分几何攻击均具有较强的鲁棒性。     

A new robust reference logo watermarking scheme

In this paper, a new yet simple reference logo watermarking scheme based on fractional Fourier transform (FrFT) and singular value decomposition (SVD) is proposed. The core idea of the proposed scheme is to segment host image into non-overlapping blocks by the means of Hilbert space filling curve and a reference image is formed by considering Human visual system (HVS). First, reference image is transformed into FrFT domain and embedding is done by modifying singular values of the reference image using singular values of watermark. After embedding, modified reference image is segmented into blocks and these modified blocks are mapped into their original places for constructing watermarked image. For extraction, a reliable watermark extraction scheme is proposed. The experimental results demonstrate better visual imperceptibility and resiliency of the proposed scheme against intentional or un-intentional variety of attacks.     

基于灰关联的自适应灰度图像信息隐藏算法

以灰关联理论和小波变换为基础,提出一种具有良好透明性和鲁棒性的信息隐藏算法.使用自定义压缩方法对预嵌入的水印灰度图像进行压缩后置乱编码.根据灰关联度的大小在载体图像的小波变换域中选取相对非平滑区作为秘密信息隐藏的区域.对区域进行DCT变换后尽量选取中高频系数中绝对值大的系数作为秘密信息嵌入位.根据小波域的人眼视觉掩蔽特性确定嵌入的强度.实验表明,文中算法具有比较好的透明性,而且能够对一些常见的攻击如噪声、几何剪切、JPEG压缩、滤波等均具有较好的鲁棒性.     

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.     

Wavelet-based image watermarking with visibility range estimation based on HVS and neural networks

This work proposes a wavelet-based image watermarking (WIW) technique, based on the human visible system (HVS) model and neural networks, for image copyright protection. A characteristic of the HVS, which is called the just noticeable difference (JND) profile, is employed in the watermark embedding to enhance the imperceptibility of the technique. First, we derive the allowable visibility ranges of the JND thresholds for all coefficients of a wavelet-transformed image. The WIW technique exploits the ranges to compute the adaptive strengths to be superimposed in the wavelet coefficients while embedding watermarks. An artificial neural network (ANN) is then used to memorize the relationships between the original wavelet coefficients and its watermark version. Consequently, the trained ANN is utilized for estimating the watermark without the original image. Many existing schemes require the original image to be involved in the calculation of the JND profile of the image. Finally, computer simulations demonstrate that both transparency and robustness of the WIW technique are superior to that of other proposed methods.