奇偶性分类下大容量医学图像可逆水印算法

提出了一种针对医学图像的基于奇偶性的大容量可逆水印算法.相比一般图像,医学图像拥有更多接近0值的像素点,在嵌入过程中容易溢出.通过测试选择像素中嵌入水印信息不会引起溢出的像素携带水印信息.本算法充分利用了医学图像中所有能携带水印信息的像素,提高了医学图像的嵌入率.为保证水印信息提取端图像像素的正确分类和算法的可逆性,本算法基于像素预测误差的奇偶性改进了提取端的测试算法.实验分析表明,本算法不仅保证了含水印医学图像的视觉效果,同时提高了医学图像的水印嵌入容量.对于一般图像,在水印嵌入量和视觉效果方面也有一定的提升.     

基于奇偶性的大容量医学图像可逆水印算法

本文提出了一种针对医学图像的基于奇偶性的大容量可逆水印算法。相比一般图像医学图像拥有更多接近0 值的像素点,在嵌入过程中容易溢出。通过测试选择像素中嵌入水印信息不会引起溢出的像素携带水印信息。本算法充分利用了医学图像中所有有能力携带水印信息的像素,提高了医学图像的嵌入率。为保证水印信息提取端图像像素的正确分类和算法的可逆性,本算法基于像素预测误差的奇偶性改进了提取端的测试算法。实验分析表明,本算法不仅保证了含水印医学图像的视觉效果,同时提高了医学图像的水印嵌入容量。对于一般图像,在水印嵌入量和视觉效果方面也有一定的提升。     

基于单向预测误差扩展的三维医学图像可逆水印算法

对于可逆水印技术在三维医学图像中的应用问题，提出一种基于单向预测误差扩展的三维医学图像可逆水印算法。首先根据像素间的三维梯度变化预测像素从而得到预测误差；然后结合磁共振成像生成的三维医学图像的特征，采用单向直方图位移与预测误差扩展相结合的方法将外部信息嵌入至三维医学图像；最后，重新预测像素，提取外部信息，恢复原始三维图像。实验结果表明，在MR-head和MR-chest测试数据体上，与二维梯度预测相比，所提算法预测误差的平均绝对偏差分别降低1.09和1.40，每个像素的最大嵌入容量分别增加0.0456比特和0.1291比特，从而能够更准确地预测像素值，嵌入更多的外部信息。该算法可应用于对三维医学图像的篡改检测以及患者隐私保护。     

基于LASSO的可逆图像水印算法

对于采用差值扩展-直方图平移的可逆水印算法,提高预测的准确度有利于减小预测误差,从而在同等嵌入失真时获得更大的嵌入容量。为了进一步提高图像像素预测的准确度,构造了一种基于LASSO （Least Absolute Shrinkage And Selection Operator）的局部预测算法。具体而言,根据图像存在边缘、纹理方向的特点,将图像像素预测问题表征为基于LASSO的优化问题;然后通过优化求解得到预测系数,进而得到预测误差;随后利用预测误差,结合差值扩展-直方图平移嵌入技术设计可逆图像水印算法。实验仿真结果表明,与当前预测性能较好的、基于最小二乘局部预测的可逆图像水印算法相比,所提算法在嵌入相同的数据时拥有更高的峰值信噪比（PSNR）。     

基于矢量直方图迁移的视频加密域可逆隐写方案

针对压缩域视频隐藏算法嵌入容量低、不可见性差的问题,提出了一种H.264/AVC加密域的可逆隐写方案。首先由嵌入容量和载体大小决定参考帧间隔参数,并根据需要决定是否对载体进行加密;然后,根据待嵌视频帧数生成嵌入密钥;最后通过压缩视频中矢量直方图迁移,实现运动矢量上的可逆信息嵌入。所提方案通过指定解码参考帧,克服了由于运动矢量修改而造成的失真累加效应。所提方案兼容基于运动矢量的视频加密算法,视频的解密和信息提取分别依赖解密密钥和嵌入密钥,两者之间相互分离,在视频密文域或者解密后的明文域中均能提取信息并无损恢复视频载体。信息的安全性依赖于嵌入密钥,密钥长度可以根据需要控制,最大长度等于可嵌入信息的帧数。实验表明该方案计算复杂度低,安全度高,并可以根据嵌入负载调整容量和不可见性,与BCH码可逆嵌入方案相比PSNR值提高3~5 dB,平均嵌入容量增加5~10倍。     

基于采样预测和差值变换的医学图像可逆水印算法

针对远程诊疗应用场景下医学图像传输的安全性问题,提出一种基于采样预测差值变换的医学图像可逆水印算法.首先对图像进行采样并预测得到相邻子样图像的像素值;然后计算得到子样图像间的像素差值,通过调整差值嵌入水印信息并形成水印图像.接收端在水印图像没有遭到篡改情况下能正确提取出水印信息并且无失真地还原原始图像.算法充分利用图像子样像素之间的高相关性,提高了嵌入容量;另外,算法具有较好的图像质量和运行效率,实验结果证明了算法的优越性.     

基于模式特征的H.264/AVC可逆视频水印

目的 针对当前可逆视频水印隐蔽性和篡改定位能力不足问题,提出一种新颖的基于帧内预测模式的可逆视频水印算法。方法 首先,深入分析I帧亮度分量的预测模式对不同类型篡改的敏感性,提取每个帧内编码宏块的预测模式,通过预测模式生成特征码。然后,结合H.264/AVC编码特性和误差补偿算法,在每个亮度4×4残差块中筛选出误差最小系数。最后,运用差值扩展的方法将特征信息作为水印可逆的嵌入到所选系数。结果 在含水印视频未受到篡改时,解码端提取水印后可对原始视频进行无损恢复。当视频受到篡改时,算法能精确定位篡改区域并且篡改定位精度达到4×4子块级。由于水印嵌入在误差最小的系数中,能够有效地降低水印嵌入对于视频质量的影响,嵌入水印后图像的PSNR值比现有的基于H.264/AVC可逆水印方案平均提高10%,测试序列的码率增量平均降低了22%左右。结论 本文算法较现有算法具有更好的嵌入率、隐蔽性、篡改检测精度, 适用于医学、军事、卫星等领域。     

基于图像插值和直方图平移的可逆水印算法

现有的基于直方图的可逆水印算法嵌入容量较少,针对这一问题,结合图像放大后像素点的特征,提出一种新的可逆水印算法。利用插值方法将图像放大4倍,既增加了图像的像素个数,又增强了像素之间的相关性;使用上下左右4个像素点计算像素点的预测误差值,并构造直方图,在峰值点处嵌入水印信息;利用相反的过程提取水印,并根据插值原理恢复原始图像。实验结果表明,将图像放大后再嵌入,可嵌入水印容量提高了3倍。算法不可见性较好、嵌入容量较大,可用于图像隐藏、信息安全等领域。     

基于直方图平移可逆水印的性能估计

多层直方图平移可逆水印算法可以获得较高的嵌入容量。由于多层嵌入复杂度较高,在实际实施多层嵌入前,一般不可预知算法是否满足用户要求的嵌入容量和水印图像质量。提出一种快速性能估计方法,该方法无需实际嵌入操作,仅根据直方图信息就可以准确估计多层嵌入后水印容量及相应的水印图像质量。实验结果表明：该方法速度快、估计精度高,为实际使用直方图平移可逆水印算法提供了一种快速、有效的判决依据。     

谱段交织预测的多光谱图像可逆信息隐藏算法

针对现有可逆信息隐藏算法嵌入容量不高并且不适合多光谱图像的问题,提出一种基于谱段交织预测的多光谱图像可逆信息隐藏算法.利用含秘图像像素值与原始图像像素值相差不大于1的特点,结合含秘谱段对多光谱图像进行多波段预测,通过预测误差直方图移位技术嵌入秘密信息.Landsat卫星和Terra卫星多光谱图像仿真结果表明,提出的算法和典型可逆信息隐藏算法相比具有更好的视觉质量和隐藏容量.     

基于运动估计的视频隐写检测算法

提出一种基于运动估计的的视频隐写检测算法。通过块均方误差的变化研究信息嵌入对运动估计的影响,发现运动矢量对隐写具有敏感性,且分块越小,对隐写越敏感。反映视频时变特性的运动矢量场被作为隐写检测中视频数据的表征,特征提取过程中,先取定分块大小,使用最小均方误差块匹配运动估计算法,得到运动矢量场。再提取运动矢量场3个方向相邻元素的共生矩阵,使用共生矩阵的主对角线及其相邻元素作为特征。使用支持向量机分类器实施检测,实验结果表明文中算法与Budhia的算法相比,具有更好的检测性能。     

Reversible video watermarking through recursive histogram modification

In this study, a new reversible video watermarking method is developed. Unlike the existing reversible video watermarking algorithms that are based on motion compensated prediction or interpolation error expansion, motion compensated interpolation errors in the proposed method are watermarked in a totally different manner with the aid of recursive histogram modification algorithm developed recently for the purpose of reversible image watermarking. However,the recursive histogram modification can not be used directly for reversible video watermarking since the important problems of ensuring reversibility for each frame and distribution of total capacity among frames are encountered. In this study, novel ideas are proposed to solve the aforementioned two problems. The proposed method is tested on the video sequences commonly used in the literature. It is shown to give better performance than the existing reversible video watermarking algorithms in terms of capacity and distortion by means of computer simulations. Also, a numerical example is provided.     

Reversible watermarking method based on asymmetric-histogram shifting of prediction errors

This paper tries to provide a new perspective for the research of reversible watermarking based on histogram shifting of prediction errors. Instead of obtaining one prediction error for the current pixel, we calculate multiple prediction errors by designing a multi-prediction scheme. An asymmetric error histogram is then constructed by selecting the suitable one from these errors. Compared with traditional symmetric histogram, the asymmetric error histogram reduces the amount of shifted pixels, thus improving the watermarked image quality. Moreover, a complementary embedding strategy is proposed by combining the maximum and minimum error histograms. As the two error histograms shift in the opposite directions during the embedding, some watermarked pixels will be restored to their original values, thus the image quality is further improved. Experimental findings also show that the proposed method re-creates watermarked images of higher quality that carry larger embedding capacity compared to conventional symmetric histogram methods, such as Tsai et al.’s and Luo et al.’s works.     

A data embedded video coding scheme for error-prone channels

We propose a novel data embedding scheme for fractional-pixel based video coding algorithms such as H.263 and MPEG-2. By modifying the motion estimation procedure at fractional-pixel precision, two bits data can be embedded in a motion vector (MV) for an inter-mode coded macroblock (MB). For half-pixel precision motion estimation, the resulted bitstream is compatible with the current video coding standards. Performance of the proposed data embedding scheme in terms of compression efficiency is also studied. As an application example of the proposed data embedding scheme, an error-resilient video coding scheme is also presented where some redundant information, which is used to protect MVs and coding modes of MBs in one frame, is embedded into the MVs in the next frame. When errors occur in one group of blocks (GOB), the decoder can exactly recover the MVs of the corrupted GOB if the next frame can be received correctly. Simulations show that the proposed scheme has better performance than standard H.263 coding scheme for transmission over error-prone channels     

基于运动矢量多直方图修正的视频可逆隐写算法

针对基于直方图平移的视频隐写算法与视频内容相关性不强进行了研究,提出了一种基于运动矢量多直方图修正的H.264视频可逆隐写算法。该算法通过统计特定帧中宏块移动的速度特性和散乱度对人眼视觉的敏感程度设计修正参数,再根据修正参数设计多个二维直方图,最后在非特定帧中通过二维直方图修正算法嵌入信息。实验结果表明：与同类型视频隐写算法相比,嵌入容量平均提高8.2%,在满嵌时该算法的PSNR和SSIM的变化值分别平均降低19.9%和17.5%,该算法在保持较低的比特率变化下,具有更好的嵌入容量和不可感知性。     

An adaptive reversible data hiding scheme based on prediction error histogram shifting by exploiting signed-digit representation

A prediction error histogram shifting (PEHS)-based reversible data hiding scheme is proposed in this paper. A novel representation for the secret stream, called signed-digit representation, is proposed to improve the image quality. The secret binary stream is first converted into a signed-digit stream, which results in a high occurrence of ‘0’. Meanwhile, a block-wise-based prediction is performed on the original image to generate prediction errors, which lead to a sharp prediction error histogram. Then, the converted signed-digit stream is embedded into the prediction errors according to the improved histogram shifting (HS)-based scheme with multiple selected peak points, resulting in an adaptive embedding capacity. The experimental results validate that the proposed scheme outperforms state-of-the-art schemes in terms of embedding capacity while maintaining a good image quality.

     

Reversible data hiding for high quality images using modification of prediction errors

In this paper, a reversible data hiding scheme based on modification of prediction errors (MPE) is proposed. For the existing histogram-shifting based reversible data hiding techniques, though the distortion caused by embedding is low, the embedding capacity is limited by the frequency of the most frequent pixel. To remedy this problem, the proposed method modifies the histogram of prediction errors to prepare vacant positions for data embedding. The PSNR of the stego image produced by MPE is guaranteed to be above 48 dB, while the embedding capacity is, on average, almost five times higher than that of the well-known Ni et al. techniques with the same PSNR. Besides, MPE not only has the capability to control the capacity-PSNR, where fewer data bits need less error modification, but also can be applied to images with flat histogram. Experimental results indicate that MPE, which innovatively exploits the modification of prediction errors, outperforms the prior works not only in terms of larger payload, but also in terms of stego image quality.     

基于帧间预测的视频水印算法

数字水印技术是视频版权保护的一种重要方法。研究了一种新的鲁棒型视频水印算法,水印嵌入时,对原始视频亮度分量的帧图像整体做DCT变换,将扩频序列叠加在其低频系数段上。水印检测时,首先基于运动估计对水印视频做帧间预测,把得到的运动补偿帧与当前帧相减,并对该残差图像整体做DCT变换,取相应低频系数段作为估计序列,将该序列与原始水印序列的相关性作为水印是否存在的判决依据。实验证明该算法具有良好的鲁棒性。     

利用运动向量差值改善深度学习视频隐写分析

目的 针对现有深度学习视频隐写分析网络准确率不够高的问题,本文从视频压缩编码的原理出发,发掘嵌密编码参数与其他参数之间的关系,通过拓展检测空间,构造新的检测通道,改善现有深度学习视频隐写分析网络的检测性能。方法 以H. 265/HEVC(high efficiency video coding)压缩视频为例,首先通过分析运动向量的嵌密修改对运动向量差值的影响,指出可将运动向量差值作为新增的采样对象(或称检测对象);接着,提出一个构造运动向量差值检测矩阵的方法,解决了空域上采样样本稀疏、时域上样本空间位置无法对齐的问题;最后,将运动向量差值矩阵直接用于改善现有的VSRNet(video steganalysis residual network)、SCA-VSRNet(selection-channel-aware VSRNet)以及Q-VSRNet(quantitative VSRNet)等3个H. 265/HEVC深度学习视频隐写分析网络,分别得到IVSRNet(improved VSRNet)、SCA-IVSRNet(selection-channel-aware improved...     

Subjectively adapted high capacity lossless image data hiding based on prediction errors

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.