A local variance-controlled reversible data hiding method using prediction and histogram-shifting

The stego image quality produced by the histogram-shifting based reversible data hiding technique is high; however, it often suffers from lower embedding capacity compared to other types of reversible data hiding techniques. In 2009, Tsai et al. solved this problem by exploiting the similarity of neighboring pixels to construct a histogram of prediction errors; data embedding is done by shifting the error histogram. However, Tsai et al.’s method does not fully exploit the correlation of the neighboring pixels. In this paper, a set of basic pixels is employed to improve the prediction accuracy, thereby increasing the payload. To further improve the image quality, a threshold is used to select only low-variance blocks to join the embedding process. According to the experimental results, the proposed method provides a better or comparable stego image quality than Tsai et al.’s method and other existing reversible data hiding methods under the same payload.     

Multiple predictors hiding scheme using asymmetric histograms

In recent years, many data hiding techniques have been proposed, and they can be generally classified into two types according to the reversibility of the image; these two types are reversible and irreversible data hiding. This study focused on reversible data hiding, which makes recovering the cover image possible after the secret data has been extracted. In 2013, Chen et al. proposed an asymmetric-histogram reversible data hiding method. In their scheme, two prediction error histograms (maximum and minimum error histograms) were used to embed the secret message. Two histograms were shifted in opposite directions. Hence, some stego-pixels were shifted to their original values. The complementary embedding strategy is effective. However, the predictor in the method is rough. Only neighboring pixels were used to generate the prediction errors, thereby resulting in poor prediction efficiency. To enhance the prediction efficiency, this paper combines several well-known predictors such as gradient adjusted gap (GAP), median edge detect, and interpolation by neighboring pixel (INP) to generate prediction errors. Different predictors along with the asymmetric-histogram method can achieve better results. The predictor GAP used more neighboring pixels to obtain the prediction value; therefore, it is suitable for complex images. However, the predictor INP only considers that closer pixels can achieve great results for smooth images. Hence, the proposed scheme combines GAP and asymmetric histogram for complex images. However, the predictor INP along with asymmetric histogram is used for smooth images. Experimental results showed that the PSNR value of the proposed method is greater than that of the asymmetric-histogram shifting method and other recent approaches.     

基于非对称直方图平移的可逆信息隐藏算法

利用两个非对称直方图分别向相反方向平移嵌入信息,会产生像素值的补偿还原效应,提出了一种更好的像素值预测方法,生成两个更偏向0值右侧和左侧的非对称预测误差直方图,这样的两个直方图在进行第二层信息嵌入时,会出现更多的像素点恢复到原始图像像素值,减少图像扭曲失真,提高载密图像质量。与传统算法相比,减少了参与直方图修改的像素量,进一步保护了载密图像质量。     

基于帧间预测误差扩展的可逆视频水印

为了提高可逆视频水印的嵌入容量和嵌水印视频质量,提出一种使用运动估计和预测误差直方图修改的算法.在嵌入端,根据视频中相邻帧间的内容关系,对每一帧使用运动估计获得其像素的预测误差,生成预测误差直方图,并通过扩展位于直方图峰值点的预测误差来嵌入水印;在每帧的嵌入过程中会产生少量用于提取水印和还原视频的头信息,包括运动向量和边界表等,将其与水印一起嵌入到该帧的参考帧中.在提取端,先获取参考帧中的水印和头信息并还原参考帧,保证提取操作具有正确的上下文,然后根据获取的头信息和还原后的参考帧提取当前帧中的水印.实验结果表明,采用文中算法获得的预测误差直方图具有高度的集中性,并且对像素值的修改十分微小,比其他可逆水印算法具有更大的嵌入容量和更好的嵌水印视频质量.     

基于中值预测的四轮嵌入可逆信息隐藏算法

传统基于预测误差直方图平移的可逆信息隐藏算法大多通过固定顺序来扫描原始图像,从而进行数据嵌入,这种方式没有考虑图像本身的纹理信息,导致无效移位像素点较多,伪装图像视觉质量较差。为解决该问题,提出一种基于中值预测的四轮嵌入可逆信息隐藏算法,以在提高嵌入容量的同时降低伪装图像的失真率。利用相邻像素之间具有较强相关性的特点,在较小的误差值处聚集大量像素点,以得到更陡峭的预测误差直方图并提高嵌入容量。对每个像素点定义复杂度,根据复杂度的高低对预测误差进行排序,优先在图像平滑区域嵌入数据,从而有效减少无效移位像素点个数,降低伪装图像的失真率。实验结果表明,该算法的最大嵌入率可以达到0.3 bpp,在0.1 bpp的嵌入率下峰值信噪比高达55.15 dB,与非对称直方图算法、误差直方图移位算法等相比,其具有较高的嵌入容量和较小的视觉失真率。     

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。     

Reversible data hiding scheme with edge-direction predictor and modulo operation

Since the first histogram shifting technique was proposed by Ni et al., many histogram based data hiding methods were proposed to improve their scheme. One of the methods is using difference value between cover image and prediction image. Another method is using two point pairs and absolute value for improving Ni et al.’s scheme. In this paper, novel reversible data hiding scheme with edge-direction predictor and modulo operation was proposed for improving histogram shifting technique with 3 cases. We considered as much as possible around pixels by using edge-direction predictor with odd and even line embedding Also, we utilize two point pairs and absolute value at the same time by using modulo operation with wrap around. In the experimental results, the proposed scheme shown a good quality image result about 48dB as similar as other schemes and enhanced hiding capacity over 50 % than other schemes.     

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.

     

基于预测误差和直方图对的无损数据隐藏方法

针对目前存在的大部分图像数据隐藏方法的嵌入量不大和视觉效果一般的问题,提出一种基于预测误差和直方图对的无损数据隐藏方法。在多灰度图像中,用某一像素点周围的八个像素点来预测中心像素点的灰度值,同时得到预测误差。再利用直方图对的方法,把数据嵌入到预测误差中。在嵌入过程中,以嵌入阈值和起伏阈值作为指导进行嵌入,通过调整这两个阈值,得到最佳的嵌入效果。实验表明,该方法在视觉效果和嵌入容量方面都取得很好的效果。     

A data hiding scheme using the varieties of pixel-value differencing in multimedia images

In this paper, a capacity promoting technique is proposed for embedding data in an image using pixel-value differencing (PVD). The PVD scheme embeds data by changing the difference value between two adjacent pixels so that more data is embedded into two pixels located in the edge area, than in the smooth area. In order to increase the embedding capacity, a new approach is proposed in this paper by searching edge area more flexibly. Instead of processing a pair of pixels at a time as proposed by Wu and Tsai, two pairs of pixels in a block are processed at the same time. In addition, we proposed a pixel-value shifting scheme to further increase the chances for embedding data. Our scheme exploits the edge areas more efficiently, thus leading to an increase in embedding capacity as shown by experimental results compared to Wu and Tsai's method. Also, the embedding result of our scheme passes the Fridrich et al.’s detection. Besides, according to the distribution of difference values, more practical range partitions are suggested for improving capacity.     

Inter-view local texture analysis based stereo image reversible data hiding

To enhance security of three-dimensional images, an inter-view local texture analysis (ILTA) based stereo image reversible data hiding method is presented. Due to low accuracy of existing predictors, two novel predictors are proposed to improve the prediction precision. In the first predictor, a texture analysis model is built by using ILTA, in which the texture similarity between a pair of matched pixels in the stereo image is used to classify pixels into horizontal texture, vertical texture, smooth and complex types. Thus, the accurate prediction is adaptively computed by considering the pixel type. Moreover, an intra-view based predictor as the second predictor is also described to predict pixels by optimal weights finding (OWF). Since ILTA and OWF predictors are combined to predict pixels in the stereo image, sharp prediction error histograms of two views are both constructed, and then multi-level histogram shifting is used to embed secret data reversibly for obtaining low image distortion and high embedding capacity. Experimental results demonstrates that ILTA and OWF predictors can obtain precise predicted values, and the proposed data hiding method outperforms some state-of-the-art data hiding methods in terms of embedding capacity and quality of stego stereo image.     

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。     

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.     

图像分区选择的像素值排序可逆数据隐藏

目的 基于像素值排序（PVO）的数据隐藏算法因其高保真的优越性受到广泛重视,并不断得到改进。本文提出一种图像分区选择思想,以进一步充分利用图像的嵌入空间,改善PVO算法的嵌入性能,提高载秘图像的信噪比。方法 原始PVO算法通常采用预测差值“1”进行数据隐藏,对平滑像素组有较好的利用率和隐蔽性,而对毛躁像素组隐秘性能明显下降,算法性能与图像像素分布情况密切相关。本文在PVO算法基础上提出图像分区选择的思想,首先,将原始图像分为若干区域,然后按移位率从小到大的顺序依次选择图像区域;其次,在每个区域中选择合适的嵌入预测误差;最后,按顺序在被选区域利用该区域的最优嵌入差值完成信息嵌入。结果 假设将图像划分为8×8个区域,对本文算法与原始PVO算法进行比较,当嵌入量为1×10⁴ bit时,Elaine图像的移位率由81.59%降为74.40%,载秘图像的峰值信噪比（PSNR）值由55.388 2提高为56.996 9,提高了1.608 7,采用其他图像并就不同嵌入量进行实验,各图像PSNR值均表现出不同程度的提高。其次,将图像分别划分为2×2、4×4、8×8、16×16个分区,当嵌入量为1×10⁴ bit时,Lena图像PSNR由原始PVO的59.204 6逐渐增加至60.846 9,其他图像在不同嵌入量时PSNR均随着分区数的增加而有不同程度的提高。结论 本文提出的基于图像分区选择的改进PVO算法,可根据像素分布情况增加对嵌入空间的利用,在相同嵌入量情况下,改进后的算法能够获得更高的PSNR值;在一定分区数量条件范围内,分区数量与图像PSNR值表现出正相关性,随着分区数量的增加,图像PSNR值随之增加;本文方法在一定程度上改善了嵌入容量,弥补了因分区数量增加带来的辅助信息增加的问题。     

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.     

A low-cost method for reliable ownership identification of medical images using SVM and Lagrange duality

Prevention of false positive and false negative errors is a major challenge for ownership identification and proof of ownership applications using digital image watermarking. Such errors are more critical with sensitive data, such as electronic patient records (EPRs) in medical image watermarking. A false positive error is a watermark detection error, which means that a watermark is detected in a media where there is no watermark. In contrast, a false negative error is an inability of the watermark detector to detect an embedded watermark in a watermarked image. These errors make ownership assessments unreliable, and the incorrect ownership identification of a patient’s record could result in failure of the correct diagnostics and treatments. To address this type of problem, a low-cost technique based on a support vector machine (SVM) and Lagrange duality was proposed to achieve reliable approximations for ownership identification in medical image watermarking without requiring the correction of attacked watermarked images. In this technique, the results of the ownership evaluation are categorized into two independent classes, namely watermark-detected and watermark-not-detected, and higher geometric margins between these classes are associated with higher reliability. To address additional situations with false positive and false negative errors, four different situations, including watermarked, unwatermarked, attacked watermarked and attacked unwatermarked images, were investigated. Experiments were conducted on duo-ISB-bit-plane (BiISB) watermarking using the histogram intersection (HI) technique as a testing platform under JPEG2000 and JPEG image compression attacks and using two groups of images: standard image processing images and X-ray medical images. The experimental investigations revealed that the HI technique guarantees that the rightful owner can be reliably identified even after severe attacks and in the face of context similarities between the watermark and the embedding pixels of the host image.     

Reversible data hiding based on block median preservation

This paper proposes a reversible data hiding scheme for gray level images. It exploits the high correlation among image block pixels to produce a difference histogram. Secret data is embedded based on a multi-level histogram shifting mechanism with reference to the integer median of each block. The image blocks are divided into four categories due to four corresponding embedding strategies, aiming at preserving the medians during data embedding. In decoder, the median pixels are retrieved first followed by the hidden data extraction, and the host image can be accurately recovered via an inverse histogram shifting mechanism after removing the secret data from the marked image. Experimental results validate the effectiveness of our scheme and demonstrate that it outperforms several previous methods in terms of capacity and marked image’s quality.     

结合层次结构和直方图平移的无损数据隐藏

针对现有基于差值直方图方法利用原始图像结构关系上的不足,提出了一种基于层次结构和差值直方图平移的无损数据隐藏方法RDH-HSDHS。RDH-HSDHS利用原始图像数据块中像素的差值形成直方图,充分利用图像中相邻像素间的相关性嵌入数据,为了进一步利用数据块中的参考像素进行数据嵌入,将参考像素组成新的图像进行下一层水印嵌入,直到当前层的嵌入容量小于解码所需的附加信息的长度或隐秘图像质量小于给定阈值。实验仿真结果表明,RDH-HSDHS能较好利用原始图像的全局和局部特性,在嵌入容量和隐秘图像质量之间达到较好的折中,在隐秘图像质量超过30 dB的同时,嵌入容量大于0.75 bit/pixel。另外,与相似方法的性能比较证明了提出方法的优势。该方法能有效应用于高质量需求的图像载体中进行信息隐藏。     

Reversible data hiding for high quality images exploiting interpolation and direction order mechanism

Reversible data hiding can restore the original image from the stego image without any distortion after the embedded secret message is exactly extracted. In this paper, a novel, reversible, data hiding scheme for high quality images is proposed in spatial domain. To increase embedding capacity and enhance image quality, the proposed scheme classifies all pixels as wall pixels and non-wall pixels. For wall pixel, the interpolation error is used to embed secret data over the interpolation prediction method. In contrast, the difference value between the non-wall pixel and its parent pixel, which is defined by the direction order, is computed to hide secret data based on the histogram shifting. The experimental results demonstrate that our scheme provides a larger payload and a better image quality than some existing schemes. Moreover, the performance of the proposed scheme is more stable for different images.