Reversible data hiding based on the local smoothness estimator and optional embedding strategy in four prediction modes

Four new prediction modes are proposed in this paper, each of which is a three-step process for all to-be-embedded pixels (nearly three-fourths of all the pixels). By designing each mode reasonably, all to-be-embedded pixels can be predicted with high accuracy, and thus, the number of embeddable pixels can be increased largely. In each step, a local smoothness estimator is utilized to determine if one embeddable pixel is located in a smooth or complex region, which is defined as the variance of the total neighbors of this pixel. In fact, the correlation evaluated by using the total neighbors, instead of a part, can reflect the complexity of the region more accurately. In this paper, an optional embedding strategy is introduced so as to select a low-distortion reversible data hiding (RDH) method according to the desired embedding rate (ER). Specifically, when the required ER is low, difference expansion (DE) is used to process those pixels in smooth regions while leaving the rest unaltered. With ER largely increased, adaptive embedding is used to embed 2-bit into these pixels with low local variance by DE while 1-bit into the remaining ones. The experimental results also demonstrate the proposed method is effective.     

Reversible watermarking based on multiple prediction modes and adaptive watermark embedding

A new reversible watermark scheme based on multiple prediction modes and adaptive watermark embedding is presented. Six prediction modes fully exploiting strong correlation between any pixel and its surrounding pixels, are designed in this paper. Under any prediction mode, each to-be-predicted pixel must be surrounded by several pixels (they constitute a local neighborhood, and any modification to this neighborhood is not allowed in the embedding process). This neighborhood has three main applications. The first one is that when it is exploited to interpolate some to-be-predicted pixel, the noticeable improvement in prediction accuracy is obtained. The second one is that its variance is employed to determine which classification (i.e., smooth or complex set) its surrounded pixel belongs to. For any to-be-predicted pixel, the number of embedded bits is adaptively determined according to this pixel’s belonging. The last one is that we can accurately evaluate the classification of watermarked pixels by analyzing the local complexity of their corresponding neighborhoods on the decoding side. Therefore, the payload can be largely increased as each to-be-predicted pixel in the smooth set can possibly carry more than 1 bit. Meanwhile, the embedding distortion is greatly controlled by embedding more bits into pixels belonging to smooth set and fewer bits into the others in complex set. Experimental results reveal the proposed method is effective.     

Reversible watermarking based on two embedding Schemes

Two different embedding schemes are presented in this paper. One aims to increase rate-distortion performance at low embedding rates. It will increase performance at low embedding rates from the following three main aspects: 1) a local variance-controlled mechanism, 2) a better predictor and 3) a new embedding scheme which can decrease the number of the pixels to be modified on the basis of providing a certain embedding capacity. Since the first scheme is only to provide low embedding rate with high visual quality, another scheme is designed to achieve higher embedding rate with good visual quality. In the second scheme, the center pixel of a three-pixel set is the prediction of a pixel, and thus any modification to it is meaningless. Each three-pixel set contains two differences. Based on the fact that the center pixel can not be modified, the remaining two pixels must be modified so that both of difference are shifted by 1. For instance, if both of pixels can carry 1-bit watermark and to-be-embedded bits are both 1, then two pixels must be shifted left or right by 1. Since we can not shift two difference by modifying only the center pixel, the distortion is high. To decrease distortion, the possibility that two bits are both equal to 1 is discarded in this paper. Experimental results also demonstrate the proposed method is effective.     

基于支持向量机的自适应图像水印技术

提出一种基于支持向量机的自适应图像空域水印嵌入算法.由于支持向量机与人眼视觉系统在自学习、泛化和非线性逼近等方面具有极大的相似性,算法利用支持向量机模拟人眼视觉特征,结合图像的局部相关特性,自适应地确定图像的最佳嵌入位置和嵌入强度.首先,利用无导师的模糊聚类分析方法对图像各像素进行初步的聚类,为有导师的支持向量机找到分类规则;然后,从各类别中选出隶属度超过一定阈值的像素作为支持向量机分类的训练样本集,建立支持向量机的分类模型,根据此模型对图像各像素再次分类,从而确定水印的最佳嵌入位置;最后结合图像自身的局部相关性,自适应地调整水印嵌入位置的像素值.该算法在提取水印时不需要原始载体图像.实验结果表明,此算法对多种图像处理均具有很好的稳健性和图像感知质量,其性能优于相关文献上的相近方法.     

一种新的基于域相关算法的图像易损水印技术

给出了一种在空域中嵌入有意义易损水印的算法，嵌入有意义的水印与无意义的伪随机序列相比，具有明显的优势。该文的方法，首先根据密钥随机选取一组图像像素以及它们的邻域，由邻域中像素的相关性选取出一个参考水印点，通过参考点和水印值来共同调整选出的图像像素值，从而完成水印信息的隐藏。检测时不需要原始图像。实验结果表明算法对有损压缩、数据替换等各种篡改操作具有很强的敏感性，通过归一化相关系数等方法可对数据的完整性做出结论。     

基于图像内容的非确定性的易损水印算法

给出了一种在空域中嵌入有意义易损水印的算法，算法中引入基于图像内容的非确定性来增强算法的安全性，由邻域中像素的相关性确定水印的参考点，通过参考点和相应的水印值来共同调整图像像素值，从而完成水印信息的嵌入。实验结果表明算法对数据替换、裁剪等各种篡改操作具有很强的敏感性，经过算法的后处理，算法具有良好的局部检测性能。     

Two dimensional time-frequency analysis based eigenvalue decomposition applied to image watermarking

An application of two-dimensional time-frequency analysis and corresponding two-dimensional eigenvalue decomposition for image watermarking purpose is proposed. The eigenvalue decomposition is used to provide a criterion for watermarking coefficients selection. It is primarily used to select pixels suitable for watermarking that belong to busy image regions. The watermark embedding is performed in the space/spatial-frequency domain by using middle frequency components, whose number is determined from the eigenvalue decomposition, as well. In order to provide its imperceptibility, watermark is modelled and adapted to the local frequency content of each considered pixel. For an efficient watermark modelling procedure, the concept of space-varying filtering is employed. Furthermore, the watermark detection is done within the space/spatial-frequency domain, which facilitates detection process due to the larger number of coefficients comparing to the space or frequency domain, separately. The efficiency of the proposed procedure and its robustness in the presence of various attacks is proven on the examples.     

Adaptive reversible watermarking with improved embedding capacity

Embedding capacity is one of the most important issues of the reversible watermarking. However, the theoretical maximum embedding capacity of most reversible watermarking algorithms is only 1.0 bits per pixel (bpp). To achieve a higher capacity, we have to modify the least significant bit (LSB) multiple times which definitely lowers the quality of the embedded image. To this end, this paper proposes a novel reversible watermarking algorithm by employing histogram shifting and adaptive embedding. Specifically, the amount of the embedded watermark is adaptively determined in terms of the context of each pixel. For pixels with small prediction error, we modify the second, third and even the fourth LSBs as well to embed more than one watermark bit. Consequently, the proposed method achieves the embedding capacity larger than 1.0 bpp in single-pass embedding as well as bringing relatively low embedding distortion. The superiority of the proposed method is experimental verified by comparing with other existing schemes.     

自适应偏心窗口平滑滤波图像修复

提出一种自适应平滑滤波数字图像修复新算法,采用变动中心的非线性平滑滤波算子,克服了固定中心点算子产生的边缘模糊问题。为了兼顾处理效果和计算效率,根据待修复的像素是位于图像平滑区还是边缘突变区,自适应地切换处理方法。实验表明,该算法对2种类型的像素均能取得较好的修复效果。提出用“分块校验和”方法在需要保护的图像中嵌入脆弱数字水印,一旦图像受损,即可精确得到损坏像素的定位模板,在修复中不再需要人工干预。     

Pattern-Based Data Hiding for Binary Image Authentication by Connectivity-Preserving

In this paper, a novel blind data hiding method for binary images authentication aims at preserving the connectivity of pixels in a local neighborhood is proposed. The "flippability" of a pixel is determined by imposing three transition criteria in a 3 times 3 moving window centered at the pixel. The "embeddability" of a block is invariant in the watermark embedding process, hence the watermark can be extracted without referring to the original image. The "uneven embeddability" of the host image is handled by embedding the watermark only in those "embeddable" blocks. The locations are chosen in such a way that the visual quality of the watermarked image is guaranteed. Different types of blocks are studied and their abilities to increase the capacity are compared. The problem of how to locate the "embeddable" pixels in a block for different block schemes is addressed which facilitates the incorporation of the cryptographic signature as the hard authenticator watermark to ensure integrity and authenticity of the image. Discussions on the security considerations, visual quality against capacity, counter measures against steganalysis and analysis of the computational load are provided. Comparisons with prior methods show superiority of the proposed scheme     

Lossless data hiding based on prediction-error adjustment

A novel lossless data hiding scheme based on a combination of prediction and the prediction-error adjustment (PEA) is presented in this paper. For one pixel,its four surrounding neighboring pixels are used to predict it and 1-bit watermark information is embedded into the prediction-error. In traditional approaches,for the purpose of controlling embedding distortion,only pixels with small prediction-errors are used for embedding. However,when the threshold is small,it is difficult to efficiently com-press the location map which is used to identify embedding locations. Thus,PEA is introduced to make large prediction-error available for embedding while causing low embedding distortions,and accord-ingly,the location map can be compressed well. As a result,the hiding capacity is largely increased. A series of experiments are conducted to verify the effectiveness and advantages of the proposed ap-proach.     

基于细粒度嵌入空间预留的密文域图像可逆信息隐藏方法

针对云数据管理中的用户隐私保护需求,密文域图像可逆信息隐藏受到了学术界的广泛关注.基于加密前预留空间的数据嵌入框架将载体图像分割成由图像块组成的两个独立区域,使用传统的可逆信息隐藏技术腾出数据嵌入空间,可以取得较好的性能.为了更好地利用图像的空间相关性,提出了一种细粒度的可伸缩嵌入空间预留策略.该策略将图像块重新排列,...     

基于像素值排序与块再分的可逆数据隐藏算法

基于像素值排序的可逆数据隐藏算法通过修改图像块中的最大像素和最小像素嵌入数据,但并未充分利用图像块内的每一个像素,从而影响嵌入性能。结合块再分原理,提出基于像素值排序的可逆数据隐藏算法。将原始图像划分为3×3的图像块,计算每一个图像块的局部复杂度。设计12种分块模式将局部复杂度小于阈值的图像块细分为子块A和B。根据子块A和B的不同局部特征分别采用2种不同的扫描顺序读取像素。子块A的像素序列使用次小值预测最小值和次大值预测最大值的方法,获得2个预测误差值,子块B的像素序列利用中值像素连续预测其余4个像素的方法,得到4个预测误差值。在此基础上,利用图像块中预测误差值为0和1的像素嵌入隐藏数据。实验结果表明,该算法在一个图像块中最高可嵌入6 bit的数据,在较低计算复杂度的情况下能够有效提高像素的嵌入性能。     

基于量化调制与像素块添加的二值工程图抗打印扫描水印算法

针对已有二值工程图抗打印扫描方法需要图像校正且不可见性较差的不足,本文提出了一种基于量化调制与像素块添加的二值工程图抗打印扫描水印算法。本方法首先识别出图中线段边缘部分作为水印嵌入区域,然后将水印序列进行分组,对每组水印序列根据其组号在水印嵌入区域添加参考点,再计算出量化区间,通过在量化区间相应位置添加像素块的方式嵌入水印。水印提取则可通过识别水印点直接进行,无需对图形进行校正还原。实验结果表明本算法能够抵抗打印扫描以及复印攻击,且水印容量大,不可感知性好,具有较好的应用前景。     

基于模糊聚类分析的自适应图像空间域水印技术

结合人眼的视觉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分析的自适应图像空间域水印嵌入算法．该算法首先根据人眼视觉系统(HVS)的感知特性，结合模糊c-均值(FCM)聚类分析，自适应确定出数字水印嵌入位置；然后利用图像自身的局部相关性，通过修改像素值，将二值水印图像降维置乱后自适应嵌入到宿主图像内，并且具有水印嵌入深度智能调节特性．该算法在提取水印信息时，不需要原始载体图像．实验结果表明：该自适应空间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．     

基于图像特征的鲁棒性数字水印算法

为了同时实现图像版权保护和图像内容的真伪性鉴定,提出了一种基于数字图像特征的鲁棒性数字水印算法。先利用Hessian变换提取宿主图像的特征区域,并根据每个特征区域内各个像素点的局部方向,将用于版权保护的水印信息嵌入到这些特征区域中。同时,向图像中除特征区域之外的区域嵌入用于图像内容真伪鉴定的脆弱性水印。水印提取过程是水印嵌入的逆过程,在不需要原始图像的情况下实现了版权水印和内容鉴定水印的提取。实验表明,该算法能抵杭大多数的移除攻击等几何篡改,对篡改发生的位置定位效果良好。     

A novel adaptive steganography based on local complexity and human vision sensitivity

This paper presents a novel adaptive steganographic scheme that is capable of both preventing visual degradation and providing a large embedding capacity. The embedding capacity of each pixel is dynamically determined by the local complexity of the cover image, allowing us to maintain good visual quality as well as embedding a large amount of secret messages. We classify pixels into three levels based on the variance of the local complexity of the cover image. When determining which level of local complexity a pixel should belong to, we take human vision sensitivity into consideration. This ensures that the visual artifacts appeared in the stego image are imperceptible, and the difference between the original and stego image is indistinguishable by the human visual system. The pixel classification assures that the embedding capacity offered by a cover image is bounded by the embedding capacity imposed on three levels that are distinguished by two boundary thresholds values. This allows us to derive a combination ratio of the maximal embedding capacity encountered with at each level. Consequently, our scheme is capable of determining two threshold values according to the desired demand of the embedding capacity requested by the user. Experimental results demonstrated that our adaptive steganographic algorithm produces insignificant visual distortion due to the hidden message. It provides high embedding capacity superior to that offered by a number of existing schemes. Our algorithm can resist the RS steganalysis attack, and it is statistically invisible for the attack of histogram comparison. The proposed scheme is simple, efficient and feasible for adaptive steganographic applications.     

Image Segmentation Based on Adaptive Cluster Prototype Estimation

An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.     

High-capacity PVO-based reversible data hiding scheme using changeable step size

Reversible data hiding (RDH) algorithm based on pixel-value-ordering (PVO) has received widespread attention because of its excellent performance. PVO algorithm divides the host image into non-overlapped equal-sized blocks, then achieves data embedding by modifying the maximum and minimum values of each block. Every pixel block can be a host of watermark data, so the smaller number of pixel blocks limit the embedding capacity (EC). In our work, a novel PVO with changeable step size (CPVO) is presented which can choose suitable step size based on the number of watermark data bits, even that allow one block to overlap other ones. Take the block size 2 × 2 as an example, we can set step size 2 × 1 or another one in CPVO. Consequently, with a block selection skill based on the noise level (NL) of a pixel block, CPVO can embed more hidden data bits into a host image. Compared to the original PVO-based schemes, experimental results show that our proposed scheme increases the EC by 2 ～ 3 times, and the marked image quality keeps much higher, even outperforms some other state-of-the-art works in some test images.

     

基于数独的大容量可篡改定位动态多重水印算法

提出一种基于数独的大容量动态水印算法,每像素的嵌入容量为2比特。先将原始图像分成大小为M×N像素不重叠区域,把大小为2M×N像素的若干个不同的水印嵌入到每个区域中。若加入水印后图像被篡改,则会造成篡改区域的水印不能正确提取。在任意时刻都可在感兴趣的区域中嵌入任意水印,即嵌入水印的位置和时间是按一定的协议动态确定的。实验结果表明该算法具有较大的水印嵌入容量和较高的篡改定位精确度。