基于SVR的嵌入强度自适应的图像水印算法

如何优化水印嵌入强度是数字水印研究中的关键问题，并与水印的鲁棒性和不可感知性密切相关。本文针对图像数据，基于SVR模型提出一种嵌入强度自适应的水印算法。在图像特征方面，本文选取图像的LBP算子和小波变换的低频系数，根据SSIM指标确定合适的水印嵌入强度，生成训练样本数据集并对SVR模型进行训练；在水印嵌入过程中，首先对水印进行加密等预处理，然后对图像子块进行DWT变换，运用训练好的SVR模型确定水印嵌入强度并嵌入水印，同理可根据密钥和SVR模型进行水印的提取。实验结果表明，本文提出的算法在保证图像质量和水印不可见性的前提下，能够对图像进行自适应地水印嵌入和提取，同时对噪声、压缩、剪切等攻击均具有较好的鲁棒性。     

一种新的SVM数字音频水印嵌入算法

为了取得鲁棒性与不可感知性的良好平衡,提出一种新的SVM数字音频水印嵌入算法.该算法以回归型支持向量机(SVR)理论及同步码技术为基础,首先结合数字音频内容,选取稳定的能量、过零率作为特征向量并获得SVR训练模型,然后利用SVR训练模型自适应确定嵌入位置,最后采纳量化调制策略,分别在空间域和频率域内将同步码和数字水印嵌入到音频载体内.仿真实验表明,本文算法不仅具有较好的不可感知性,而且对诸如叠加噪声、重新采样、重新量化、低通滤波、MP3压缩、随机剪切等攻击均具有较好的鲁棒性.     

基于回归型支持向量机的小波域盲水印算法

以回归型支持向量机（Support Vector Regression,SVR）理论为基础,提出了一种新的基于SVR的小波域盲水印算法。算法核心思想是先对图像进行小波分解,然后利用图像小波分解后的子图系数之间的关系和图像局部相关性获得SVR训练模型,并利用SVR训练模型在小波域嵌入和提取水印。该算法以保证鲁棒性和透明性的良好平衡为前提,实现了数字水印的盲检测。仿真实验表明,该文算法不仅具有较好的透明性,而且对JPEG压缩和一般的图像处理具有很强的鲁棒性,其整体性能明显优于现有基于SVM的空间域上的水印算法。     

可有效抵抗一般性几何攻击的数字水印检测方法

以回归型支持向量机理论为基础, 结合性能稳定的伪Zernike矩和Krawtchouk矩, 提出了一种可有效抵抗一般性几何攻击的强鲁棒数字图像水印检测算法. 该算法首先选取图像的低阶Krawtchouk矩作为特征向量, 然后利用SVR对几何变换参数进行训练学习并对待检测图像进行数据预测, 最后对其进行几何校正并提取水印信息. 仿真实验结果表明, 该数字图像水印检测算法不仅具有较好的不可感知性, 而且对常规信号处理和一般性几何攻击均具有较好的鲁棒性.     

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。     

一种新的基于数字水印的视频加密算法

提出了一种新的算法,通过对亮度块进行加密后再进行矩阵奇异值分解相结合,再对水印图像做同样处理的方案实现了视频中数字水印的快速高效注入.实验表明,该算法相比已有的其他视频数字水印加密算法具有较好的鲁棒性和不可感知性.     

一种基于HVS的模糊控制图像水印算法

本文通过对图像特点的分析,并根据人类视觉对图像边缘、纹理等区域感知的模糊性,利用模糊控制方法对非线性的、无法获得精确数学模型的复杂系统的良好控制性能,提出了一种对水印强度进行智能控制的算法,使水印强度能够完全自适应于原始图像,较好的平衡了不可见性和鲁棒性之间的矛盾.实验结果表明,按照该算法对水印进行动态嵌入,使水印具有良好的不可感知性.该算法易于实现,并有利于数字水印的实时性应用,有实际应用价值.     

一种新的大容量数字音频水印嵌入算法

提出了一种新的大容量数字音频水印嵌入算法．该算法具有以下特点：（1）以整型小波变换为基础，不仅降低了舍入误差，而且提高了音频水印的嵌入与检测速度；（2）采纳分段变换及整体修改策略，增强了数字水印嵌入的稳定性；（3）以灰度图像作为数字水印，不仅数据容量大，而且更具层次感和多样性；（4）以保证不可感知性和鲁棒性的良好平衡为前提，实现了数字水印的盲检测．仿真实验表明：所提出的算法不仅具有较好的不可感知性，而且对诸如重新采样、重新量化、低通滤波、MP3压缩、叠加噪声等攻击均具有较好的鲁棒性．     

基于HVS特性的数字图像水印研究

提出了一种基于小波变换低频系数的数字水印嵌入和检测算法.其中使用了有意义的二值图像水印来替代随机序列,先将水印通过Arnold置乱加密后再全部嵌入到低频子带系数中.该算法利用了人眼视觉系统(HVS)特性对水印嵌入强度做自适应调节以增强水印的鲁棒性和保证水印的不可感知性.实验结果表明,使用该算法嵌入的数字水印具有很好的隐蔽性,并且嵌入水印的图像对有损压缩、滤波、加入随机噪声和旋转等操作具有较强的抵御攻击能力.     

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.     

基于第二代Bandelet变换的抗几何攻击图像水印

抗几何攻击的鲁棒图像水印设计是目前水印技术研究的难点和热点之一. 文中分析了图像的Bandelet变换特性, 提出了一种以图像特征点矢量集为特征向量的回归支持向量机(Support vector regression, SVR)和第二代 Bandelet变换的抗几何攻击图像水印算法,采取的主要方法包括: 1)在Bandelet变换提取的刻画图像局部特征的几何流系数上, 采用奇偶量化嵌入水印; 2)利用Harris-Laplace算子从归一化的含水印图像中提取具有几何形变鲁棒性的图像特征点,构造特征点矢量集 作为特征向量,应用回归支持向量机对几何变换参数进行训练学习; 3)水印检测时, 先利用SVR训练模型得到待检测图像所受几何攻击的参数并作几何校正,然后通过奇偶检测器盲提取水印.仿真实验表明,所提出的水印算 法不仅具有良好的透明性,而且对常规图像处理、一般性几何攻击和组合攻击均具有良好的鲁棒性.     

A New Adaptive Digital Audio Watermarking Based on Support Vector Regression

On the basis of support vector regression (SVR), a new adaptive blind digital audio watermarking algorithm is proposed. This algorithm embeds the template information and watermark signal into the original audio by adaptive quantization according to the local audio correlation and human auditory masking. The procedure of watermark extraction is as follows. First, the corresponding features of template and watermark are extracted from the watermarked audio. Then, the corresponding feature of template is selected as training sample to train SVR and an SVR model is returned. Finally, the actual outputs are predicted according to the corresponding feature of watermark, and the digital watermark is recovered from the watermarked audio by using the well-trained SVR. Experimental results show that our audio watermarking scheme is not only inaudible, but also robust against various common signal processing (such as noise adding, resampling, requantization, and MP3 compression), and also has high practicability. In addition, the algorithm can extract the watermark without the help of the original digital audio signal, and the performance of it is better than other SVM audio watermarking schemes.     

A novel color image watermarking scheme in nonsampled contourlet-domain

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust color image watermarking scheme against geometric distortions. Based on the support vector regression (SVR) and nonsubsampled contourlet transform (NSCT), we propose a new color image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the geometrically invariant space is constructed by using color image normalization, and a significant region is obtained from the normalized color image by utilizing the invariant centroid theory. Then, the NSCT is performed on the green channel of the significant region. Finally, the digital watermark is embedded into host color image by modifying the low frequency NSCT coefficients, in which the HVS masking is used to control the watermark embedding strength. In watermark detection, according to the high correlation among different channels of the color image, the digital watermark can be recovered by using SVR technique. Experimental results show that the proposed color image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.     

一种基于SVR几何校正的数字水印检测算法

以回归型支持向量机（SVR）理论基础,提出了一种可有效抵抗几何攻击的图像水印检测新算法.该算法首先选取图像的组合矩作为特征向量,并通过SVR对旋转、缩放、平移等几何变换参数进行训练学习,以获得SVR训练模型;然后利用SVR训练模型对待检测图像进行数据预测,并结合预测输出结果对其进行几何校正;最后从已校正数字图像内提取出水印信息.仿真实验结果表明,本文算法对常规信号处理（滤波、叠加噪声、JPEG压缩等）和几何攻击（旋转、缩放、平移、剪切等）均具有较好的鲁棒性。     

Geometrically invariant image watermarking using SVR correction in NSCT domain

Based on the support vector regression (SVR) geometric distortions correction, we propose a robust image watermarking algorithm in nonsubsampled contourlet transform (NSCT) domain with good visual quality and reasonable resistance toward geometric attacks in this paper. Firstly, the NSCT is performed on original host image, and corresponding low-pass subband is selected for embedding watermark. Then, the selected low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by modulating the NSCT coefficients in small blocks. In digital watermark detecting procedure, the SVR geometrical distortions correction is utilized. Experimental results show that the proposed image watermarking is invisible, and robust against common image processing and some geometrical attacks.     

Robust digital watermarking in PDTDFB domain based on least squares support vector machine

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric distortions. Based on the least squares support vector machine (LS-SVM) geometric distortions correction, we propose a new image watermarking scheme in shiftable complex directional pyramid (PDTDFB) domain with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the PDTDFB decomposition is performed on the original host image. Then, the corresponding lowpass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by modulating the selected lowpass PDTDFB coefficients in small blocks. The main steps of digital watermark detecting procedure include: (1) the PDTDFB decomposition is performed on the test images, and some low-order Gaussian–Hermite moment energy of highpass subbands are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a LS-SVM training model can be obtained; (3) the watermarked image is corrected with the well trained LS-SVM model; and (4) the digital watermark is extracted from the corrected watermarked image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc, but also robust against the geometrical distortions.     

A new SVM-based image watermarking using Gaussian-Hermite moments

Geometric attack is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric attacks. Based on the support vector machine (SVM) and Gaussian-Hermite moments (GHMs), we propose a robust image watermarking algorithm in nonsubsampled contourlet transform (NSCT) domain with good visual quality and reasonable resistance toward geometric attacks in this paper. Firstly, the NSCT is performed on original host image, and corresponding low-pass subband is selected for embedding watermark. Then, the selected low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by modulating adaptively the NSCT coefficients in small block. The main steps of digital watermark detecting procedure include: (1) some low-order Gaussian-Hermite moments of training image are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a SVM training model can be obtained; (3) the watermarked image is corrected with the well trained SVM model; (4) the digital watermark is extracted from the corrected watermarked image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, JPEG compression, etc., but also robust against the geometric attacks.