Data hiding scheme improving embedding capacity using mixed PVD and LSB on bit plane

In data hiding, pixel-value differencing and least significant bit are well-known techniques to embed secret data. In this paper, a novel data hiding scheme is proposed to provide high embedding capacity. In case of previous methods, pixel-value differencing and least significant bit techniques were used individually or in combination based on pixel, but the proposed method utilizes first by dividing the bit region and two schemes are used simultaneously in the same bit plane. The experimental results show that the proposed method has strength on embedding capacity maintaining visual image quality without distortion to the human eyes.     

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.     

Reversible data hiding exploiting spatial correlation between sub-sampled images

Reversible data hiding enables host media to be restored from marked media without any loss of host information. Since this reversibility helps to make right decision during image analysis, it is highly desired in quality-sensitive imagery where even the minimal distortion introduced by embedding data is unacceptable. In this paper, we propose a reversible data hiding method that modifies the difference histogram between sub-sampled images. It exploits the high spatial correlation inherent in neighboring pixels to achieve high capacity and imperceptible embedding. On various test images including 16-bit images, we demonstrate the validity of our proposed method by comparing to other existing reversible data hiding algorithms. Experimental results support that our method provides high embedding capacity while keeping the distortions at a low level.     

A survey of interpolation-based reversible data hiding methods

It is called as a reversible data hiding method when the cover object can be restored together with extracting the secret data at a receiver. In reversible data hiding, interpolation-based data hiding methods are recently proposed, where image interpolation techniques are used before embedding the secret data. In this paper, reversible data hiding methods using interpolation techniques are described and analyzed on the embedding capacity and the visual image quality that many researchers have tried to improve these different measurements. It is concluded with the directions of research with some recommendations.     

A novel data hiding for color images based on pixel value difference and modulus function

This paper proposes a novel data hiding method using pixel-value difference and modulus function for color image with the large embedding capacity(hiding 810757 bits in a 512×512 host image at least) and a high-visual-quality of the cover image. The proposed method has fully taken into account the correlation of the R, G and B plane of a color image. The amount of information embedded the R plane and the B plane determined by the difference of the corresponding pixel value between the G plane and the median of G pixel value in each pixel block. Furthermore, two sophisticated pixel value adjustment processes are provided to maintain the division consistency and to solve underflow and overflow problems. The most importance is that the secret data are completely extracted through the mathematical theoretical proof.     

用于实现图像对比度增强的可逆数据隐藏算法

针对已有算法嵌入率低、算法过程复杂等问题,提出一种新的可逆数据隐藏算法。算法为了提高信息隐藏率,首先选取直方图中值点两侧的峰值进行数据嵌入。在多次嵌入之后,算法选择中值点位置嵌入所有峰值信息,由于中值点在嵌入后可以直接定位,因此可以实现可逆嵌入与恢复,从而不需要已有算法最低有效位的替换过程,减少了算法过程的复杂性。算法在两个图像集上进行性能测试,先与已有算法进行数据嵌入量比较,接着与MATLAB中3个对比度增强函数进行比较,用于评估算法的嵌入容量以及对比度增强后的图像质量。实验结果表明,所提算法在实现可逆数据隐藏和对比度增强的同时,嵌入容量有较大的提高。     

Quadruple Difference Expansion-Based Reversible Data Hiding Method for Digital Images

ABSTRACT

Lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but also the ability of cover media to be reconstructed without any distortion. A latest lossless data hiding technique is proposed by Hong Lin Jin's that is based on hiding only one data bit in the spatial domain in gray-level image. However, this method uses double difference expansion to embed bits which results in a small embedding capacity. For this purpose, we propose an improved algorithm with the potential of increasing the payload capacity and maintaining good image quality. The proposed improved algorithm is characterized by two aspects. First, the proposed improved reversible data hiding scheme is enhanced to exhibit data hiding in color palette images. Second, the embedding level is improved by using quadruple difference expansion to guarantee the embedding of 2-bit data into color images. Experiments of the proposed improved method have been conducted over several well-known test images. The results show that the proposed improved method significantly improves the embedding capacity over Hong Lin Jin's scheme by the range of 15–35% for grayscale images and 20–46% for color images while still maintaining the quality of the stego-images.     

Double linear regression prediction based reversible data hiding in encrypted images

Existing prediction-based works on reversible data hiding in encrypted images usually embed the secret messages by referring to the difference between current pixel and its predicted value. An accurate prediction model may promote an improvement of embedding capacity. Existing schemes, however, may not work well due to involving a bad prediction model so that their embedding capacity cannot be improved further. To address the problem, this paper proposes a new reversible data hiding scheme in encrypted images by designing double linear regression prediction model. Proposed model can significantly improve the prediction accuracy of current pixel based on neighboring pixels, more auxiliary rooms are thus vacated to embed secret data. Furthermore, a prediction error map is constructed to mark the error positions caused by inaccurate prediction, which can be further compressed lossless to lower the capacity of auxiliary data. Reversible recovery for original image can be finally achieved successfully. Experimental results demonstrate that the proposed scheme significantly improves prediction accuracy and data embedding capacity by combining double linear regression prediction model and prediction error map, and then can achieve separable and lossless recovery for the original image. Compared with existing works, the proposed scheme can implement a higher visual quality of decrypted images, while maintaining a larger embedding capacity.

     

自适应高容量医学图像可逆数据隐藏算法

提出了一种新的医学图像无损数据隐藏算法,根据医学图像特点,将隐秘信息分别嵌入在感兴趣区域和非感兴趣区域中。在非感兴趣区域中,采用自适应整数变换方法增大嵌入容量并控制失真;在感兴趣区域,采用最低有效位替换方法控制水印图像质量。实验结果表明,算法的嵌入容量在1.2bpp到1.7bpp之间,而峰值信噪比保持在43dB左右。与现有相关算法相比,嵌入容量具有明显优势并保持较高的图像质量。另外,本方法计算复杂度低,能方便应用于实际的医学信息系统中。     

An efficient data hiding method based on adaptive directional pixel value differencing (ADPVD)

Steganography is an important secret information communication technology in which one may send messages without others having knowledge of their existence. This paper proposes a new adaptive steganography method for color images using adaptive directional pixel-value differencing (ADPVD). The proposed method increases the capacity of the hidden secret data and improves the security of the stego-color image as well. The hiding capacity of the original PVD method is investigated by considering three directional edges: horizontal, vertical and diagonal directions using color cover image. The cover image is partitioned into 2-pixel blocks in a non-overlapping fashion and scanned in raster-scan order in all three directions. The proposed method adaptively selects the appropriate embedding directions for each color channel according to the largest embedding capacity. The security is improved since different pixel directions are employed adaptively to embed different number of message bits in each color channel. The experimental results show that the proposed method provides both larger embedding capacity and better visual quality of the stego color image compared with other PVD-based algorithms.     

A TRIBUTE TO ALF MONGÉ

Abstract

A data hiding method using three-directional pixel-value differencing for gray images is presented in this article. The cover image is divided into non-overlapping 2 × 2 sub-blocks, and the basis pixel is selected to calculate three different values of a sub-block. Difference values are replaced by embedding the number of secret bits that are referenced by the range table. For three pixel pairs in the sub-block, the optimal pixel adjustment is preceded to reduce the distortion of visual quality. The experimental results demonstrate that the proposed method provides a high capacity and good visual quality.     

整数变换实现大容量图像无损信息隐藏

目的 针对图像无损信息隐藏中嵌入容量和隐藏图像质量问题,描述了一种基于可逆整数变换算法在图像中实现较大容量数据嵌入的无损隐藏方法。方法 定义的可逆整数变换算法先计算图像块n个像素点的整数平均值,在整数变换过程中将各像素点与平均值之间的差值扩展到4倍。结果 整数变换后图像块所有像素点最低两位有效位相同,产生的冗余信息可用来嵌入2(n-1)比特数据。结论 给出了一种新的可逆整数变换算法并实现较大容量图像无损信息隐藏,实验结果表明该方法具有较大的数据嵌入容量和较好的隐藏图像质量,嵌入数据和原宿主图像均能从隐藏图像中无失真恢复。     

A high capacity reversible data hiding scheme with edge prediction and difference expansion

To enhance the embedding capacity of a reversible data hiding system, in this paper, a novel multiple-base lossless scheme based on JPEG-LS pixel value prediction and reversible difference expansion will be presented. The proposed scheme employs a pixel value prediction mechanism to decrease the distortion caused by the hiding of the secret data. In general, the prediction error value tends to be much smaller in smooth areas than in edge areas, and more secret data embedded in smooth areas still meets better stego-image quality. The multiple-base notational system, on the other hand, is applied to increase the payload of the image. With the system, the payload of each pixel, determined by the complexity of its neighboring pixels, can be very different. In addition, the cover image processed by the proposed scheme can be fully recovered without any distortion. Experimental results, as shown in this paper, have demonstrated that the proposed method is capable of hiding more secret data while keeping the stego-image quality degradation imperceptible.     

Reversible steganography based on side match and hit pattern for VQ-compressed images

Data hiding is an important technique in multimedia security. Multimedia data hiding techniques enable message senders to disguise secret data by embedding them into cover media. Thus, delivering secret messages is as easy as sending the cover media. Recently, many researchers have studied reversible data hiding for images. Those methods can reconstruct the original cover image and extract the embedded secret data from a stego-image. This study proposes a novel reversible steganographic method of embedding secret data into a vector quantization (VQ) compressed image by applying the concept of side match. The proposed method uses extra information, namely the hit pattern, to achieve reversibility. Moreover, its small hit pattern enables the embedding of the entire hit pattern along with the secret data in most cases. To optimize visual quality of the output stego-image, the method applies the concept of partitioned codebooks (state codebooks). The partition operation on the codebook uses a look-up table to minimize embedding and extraction time. We also propose the use of diagonal seed blocks to embed the entire hit pattern into the cover image without producing any extra control messages. Compared to the Chang and Lin method, the experimental results show that the proposed method has higher capacity, better visual quality, and shorter execution time.     

利用改进整数变换实现图像无损信息隐藏

为了在图像中实现无损信息隐藏,并提高数据嵌入容量和隐蔽性,给出一种基于改进整数变换的无损信息隐藏方法。首先对图像块采用改进算法进行整数变换,使得变换前后图像块引入失真较少,然后自适应选择整数变换后引入失真较小的可修改图像块进行整数变换,并嵌入较大数据量的机密信息,在公共信道进行传输后接收端能正确提取机密信息,并无损恢复原宿主图像。实验结果表明,该方法具有较大的数据嵌入容量和较好的隐蔽性,可用于医学图像中隐藏病历信息及遥感、军事图像的无损隐秘传输等领域。     

Data hiding based on the similarity between neighboring pixels with reversibility

The technique of reversible data hiding recovers the original image from a stego-image without distortion after the hidden data are extracted. A natural image usually contains several smooth areas. The difference between two adjacent pixels has a high probability of being a small value. Therefore, this study proposed a novel reversible data hiding method, Adjacent Pixel Difference (APD), which employs the histogram of the pixel difference sequence to increase the embedding capacity. Experimental results reveal that APD achieves a high embedded capacity and still maintains a high stego-image quality. Furthermore, the stego-image quality and embedded capacity of the APD method outperform those of NSAS, F&S and T&H.     

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。     

一种基于LSB和PVD的图像信息隐藏方法研究

为了提高图像中秘密信息的嵌入量,提出并实现了一种基于最低有效位和像素值差异的图像信息隐藏方法。通过计算两个连续像素值的差异来判断图像的一致区域和边界区域,在一致区域使用最低有效位方法,而在边界区域使用像素值差异方法。实验结果表明,结合这两种方法后信息的嵌入量是单纯使用像素值差异方法的1.59～1.97倍,并且隐写图像具有大于38 dB的峰值信噪比,保证了隐写图像的视觉质量。     

A lossless data embedding technique by joint neighboring coding

Information hiding methods are currently exploited by many researchers for various applications. Proposing an efficient and feasible information hiding method is valuable. This paper presents a new reversible information hiding method for vector quantization (VQ)-compressed grayscale images by using joint neighboring coding (JNC) technique. The proposed method embeds secret data by using the difference values between the current VQ-compressed index and left or upper neighboring indices. The experimental results show that the proposed method achieves the best visual quality of reconstructed images compared with the two related works. In addition, the proposed method obtains as high embedding capacity as Lin and Chang's method, followed by Yang et al.'s method. As for execution speed, Yang et al.'s method is fastest, followed by the proposed method, and then Lin and Chang's method. With respect to bit rate, the proposed method has a little higher bit rate in comparison with the two related works.