An SMVQ-based reversible data hiding technique exploiting side match distortion

Secure online communication is a necessity in today’s digital world. This paper proposes a novel reversible data hiding technique based on side match vector quantization (SMVQ). The proposed scheme classifies SMVQ indices as Case 1 or 2 based on the value of the first state codeword’s side match distortion (SMD) and a predefined threshold t. The proposed scheme uses this classification to switch between compression codes designed for Cases 1 and 2 SMVQ indices. The length of these compression codes is controlled by the parameter ?. Thus, with the selection of appropriate ? and t values, the proposed scheme achieves good compression, creating spaces to embed secret information. The embedding algorithm can embed n secret bits into each SMVQ index, where n = 1, 2, 3, or 4. The experimental results show that the proposed scheme obtains the embedding rates of 1, 2, 3, or 4 bit per index (bpi) at the average bit rates of 0.340, 0.403, 0.465, or 0.528 bit per pixel (bpp) for the codebook size 256. This improves the performance of recent VQ and SMVQ-based data hiding schemes.     

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.     

A centroid based vector quantization reversible data hiding technique

In this paper, we propose a reversible data hiding scheme that exploits the centroid formula. Specifically, we use it to define a centroid boundary vector and a centroid state codebook CSCB. Initially, our centroid boundary vectors and CSCBs are the same as the side match vector quantization (SMVQ) algorithm’s boundary vectors and state codebooks SCBs. For each VQ index, the proposed scheme exploits the centroid formula to update its centroid boundary vector and the corresponding CSCB. The updating is coupled with a heuristic to select the best state codebook (i.e., either SCB or CSCB) for each VQ index, which generates a highly compressible distribution of index values. Our experimental results show that the proposed scheme can embed n = 1, 2, 3, and 4 bit per index (bpi) at bit rates of 0.332, 0.394, 0.457, and 0.519 bit per pixel (bpp), respectively, for the main codebook size N = 256. These results confirm that the proposed scheme improves recent VQ and SMVQ based reversible data hiding schemes.

     

Representation of Edmonds' Algorithm for Finding Optimum Graph Branching on Associative Parallel Processors

In the paper, an efficient parallel implementation of Edmonds' algorithm is suggested for finding optimum graph branching on an abstract model of the SIMD type with vertical data processing (STAR machine). For this, associative parallel algorithms for finding critical circuit and its contraction, as well as for unfolding embedded critical circuits, are constructed for directed weighted graphs represented as a list of arcs and their weights. It is shown that the execution of Edmonds' algorithm on a STAR machine requires O(nlogn) time, where nis the number of graph vertexes. Basic advantages of the parallel implementation of Edmonds' algorithm compared to its implementation on sequential computers are discussed.     

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.     

Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico

A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: (1) local linear histogram matching (LLHM); (2) neighbourhood similar pixel interpolator (NSPI); (3) geostatistical neighbourhood similar pixel interpolator (GNSPI); and (4) weighted linear regression (WLR). All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients (κ), and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis (ISODATA) land-cover classification maps. In addition, Pearson correlation coefficients (r) and root mean squares of the error (RMSEs) were employed for estimates agreement with fractional land cover. The best results visually (overall accuracy > 85%, κ < 9%, quantity disagreement index < 5.5%, and allocation disagreement index < 12.5%) and statistically (r > 0.84 and RMSE < 7%) were obtained from the GNSPI method. These results suggest that the GNSPI method is suitable for routine use in reconstructing the imagery stack of Landsat ETM+ SLC-off gap-filled data for use in forest-monitoring applications in this type of heterogeneous landscape.     

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。     

On the Implementation of a Secured Digital Watermarking Framework

ABSTRACT

In this paper, a saliency-based digital watermarking system which employs a higher degree security by means of Symmetric Key Cryptography is proposed. The projected algorithm can implant more information into less perceptive areas within the original image determined by saliency map. The scheme helps to detect the regions that have better data hiding capacity within an image. The locations with less perceptibility specifies the most insignificant location from the view of visibility within an image, so any alteration within these locations will be less apparent to any spectator. Moreover, the watermark is encrypted with Symmetric Key Cryptography to make it secure from the attackers. Here the algorithm is tested by means of imperceptibility and robustness.     

Lossless data hiding for absolute moment block truncation coding using histogram modification

This paper presents a lossless data hiding method for an absolute moment block truncation coding (AMBTC) images, which is a compressed grayscale image. It is not easy to hide secret data in an AMBTC-compressed image because it is composed of bit planes. Thus, it is very sensitive to change some pixels. Nevertheless, to improve the hiding capacity, we present an efficient extension of the histogram modification technique by counting the coefficients of the bit planes in each 4 × 4 block. In addition, our proposed scheme has low complexity and achieves a high embedding capacity with the good perceptual quality compared to the prior arts. In an experiment, we verified our proposed data hiding scheme through the comparison with previous methods.     

Reversible data hiding for VQ indices using hierarchical state codebook mapping

In this paper, we present an approach to efficiently hide sensitive data in vector quantization (VQ) indices and reversibly extract sensitive data from encrypted code stream. The approach uses two patterns to compress VQ indices. When an index is equal to its upper neighbor’s index or left neighbor’s index, it is encoded by the corresponding equivalent index; otherwise, it is encoded by a modified VQ codebook mapping named as hierarchical state codebook mapping (HSCM). In the proposed scheme, the hierarchical state codebook mapping (HSCM) is main coding pattern and it is generated according to the side-match distortion method(SMD). By the above two patterns, the size of original code stream is reduced, and more storage space can be used to embed sensitive data. The experimental results indicated that the proposed scheme can achieve a higher embedding capacity than the previous state-of-the-art VQ-index-based data hiding methods.     

Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake,China

Suspended particulate matter (SPM) is a dominant water constituent of case-II waters, and SPM concentration (C_SPM) is a key parameter describing water quality. This study, using Landsat 8 Operational Land Imager (OLI) images, aimed to develop the C_SPM retrieval models and further to estimate the C_SPM values of Dongting Lake. One Landsat 8 OLI image and 53 C_SPM measurements were employed to calibrate Landsat 8-based C_SPM retrieval models. The C_SPM values derived from coincident Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) images were compared to validate calibrated Landsat 8-based C_SPM models. After the best stable Landsat 8-based C_SPM retrieval model was further validated using an independent Landsat 8 OLI image and its coincident C_SPM measurements, it was applied to four Landsat 8 OLI images to retrieve the C_SPM values in the South and East Dongting Lake. Model calibration results showed that two exponential models of the red band explained 61% (estimated standard error (SE) = 7.96 mg l^–1) and 67% (SE = 6.79 mg l^–1) of the variation of C_SPM; two exponential models of the red:panchromatic band ratio obtained 81% (SE = 5.48 mg l^–1) and 77% (SE = 4.96 mg l^–1) fitting accuracy; and four exponential and quadratic models of the infrared band explained 72–83% of the variation of C_SPM (SE = 5.18–5.52 mg l^–1). By comparing the MODIS- and Landsat 8-based C_SPM values, an exponential model of the Landsat 8 OLI red band (C_SPM = 1.1034 × exp(23.61 × R)) obtained the best consistent C_SPM estimations with the MODIS-based model (r = 0.98, p < 0.01), and its further validation result using an independent Landsat 8 OLI image showed a significantly strong correlation between the measured and estimated C_SPM values at a significance level of 0.05 (r = 0.91, p < 0.05). The C_SPM spatiotemporal distribution derived from four Landsat 8 images revealed a clear spatial distribution pattern of C_SPM in the South and East Dongting Lake, which was caused by natural and anthropogenic factors together. This study confirmed the potential of Landsat 8 OLI images in retrieving C_SPM and provided a foundation for retrieving the spatial distribution of C_SPM accurately from this new data source in Dongting Lake.     

Reversible data hiding using side-match predictions on steganographic images

Information hiding is an important method to achieve multi-media security. Recently, many researchers have paid attention to the reversible data hiding scheme, which can completely recover original multi-media files after the embedded data are extracted. In this paper, a side-match approach is proposed to achieve more capacity in histogram-based reversible data hiding for grayscale images. The histogram is created by exploiting the difference in all the values between pixels and their predictedive values. Experimental results show that our method is capable of providing a great embedding capacity without causing noticeable distortion. In one-level hiding, where it has the best capacity, our method conserves image qualities larger than 48 dB. Furthermore, in multilevel hiding, a rotation strategy is proposed to further improve image qualities. Experimental results show that our method performs better than other existing methods in multilevel hiding cases.     

Secure medical images based on data hiding using a hybrid scheme with the Hamming code,LSB, and OPAP

Some medical accidents in hospitals are related to mismanagement of medical images X-ray and computer tomography (CT). A counterfeit medical image leads to medical accidents. Thus, security of medical images is important for patient safety. Data hiding can help patients avoid mistakes of doctors or nurses who confuse patient X-rays or CT scans. A hybrid data hiding scheme was applied that combined the Hamming code and LSB with an optimal pixel adjustment process algorithm for encoding to conceal hidden patient information. In addition, quality and capacity of stego-images were improved. Experimental results confirmed that the hiding capacity provided by this strategy is greater than for other schemes. Moreover, the image quality of stego-images with the proposed scheme remained more than 50 dB for most medical test images. In this experiment, we verified the proposed steganographic data hiding scheme as RS steganalysis.     

三维图匹配算法

目的 现有的图匹配算法大多应用于二维图像,对三维图像的特征点匹配存在匹配准确率低和计算速度慢等问题。为解决这些问题,本文将分解图匹配算法扩展应用在了三维图像上。方法 首先将需要匹配的两个三维图像的特征点作为图的节点集;再通过Delaunay三角剖分算法,将三维特征点相连,则相连得到的边就作为图的边集,从而建立有向图;然后,根据三维图像的特征点构建相应的三维有向图及其邻接矩阵;再根据有向图中的节点特征和边特征分别构建节点特征相似矩阵和边特征相似矩阵;最后根据这两个特征矩阵将节点匹配问题转化为求极值问题并求解。结果 实验表明,在手工选取特征点的情况下,本文算法对相同三维图像的特征点匹配有97.56%的平均准确率;对不同三维图像特征点匹配有76.39%的平均准确率;在三维图像有旋转的情况下,有90%以上的平均准确率;在特征点部分缺失的情况下,平均匹配准确率也能达到80%。在通过三维尺度不变特征变换（SIFT）算法得到特征点的情况下,本文算法对9个三维模型的特征点的平均匹配准确率为98.78%。结论 本文提出的基于图论的三维图像特征点匹配算法,经实验结果验证,可以取得较好的匹配效果。     

Advanced image steganography based on exploiting modification direction and neutrosophic set

A new variant of image steganography based on exploiting modification directions (EMD) named advanced EMD (AEMD) is introduced. In this method the secret digits in mⁿ -ary notional systems are embedded into a group of n pixels of the cover image. To increase data hiding capacity, edge masking characteristics of human visual system is exploited to embed more bits at image edge pixels than non-edge pixels. To do this, a neutrosophic set edge detector, named as MNSED is also introduced to classify cover image into 2?×?2 non-overlapping edge and non-edge blocks without any overhead information. Hence the secret digits in two different bases are embedded into the edge and non-edge blocks. Experimental results show that the proposed method provides higher embedding capacity and better quality compared to the existing schemes, while its resistance to steganographic attack is still higher.

     

Pathological brain detection in MRI scanning via Hu moment invariants and machine learning

Abstract

Background: We proposed a new automatic and rapid computer-aided diagnosis system to detect pathological brain images obtained in the scans of magnetic resonance imaging (MRI). Methods: For simplification, we transformed the problem to a binary classification task (pathological or normal). It consisted of two steps: first, Hu moment invariants (HMI) were extracted from a specific MR brain image; then, seven HMI features were fed into two classifiers: twin support vector machine (TSVM) and generalised eigenvalue proximal SVM (GEPSVM). Results: Then, a 5 × 5-fold cross validation on a data set containing 90 MR brain images, demonstrated that the proposed methods “HMI + GEPSVM” and “HMI + TSVM” achieved classification accuracy of 98.89%, higher than eight state-of-the-art methods: “DWT + PCA + BP-NN”, “DWT + PCA + RBF-NN”, “DWT + PCA + PSO-KSVM”, “WE + BP-NN”, “WE + KSVM”, “DWT + PCA + GA-KSVM”, “WE + PSO-KSVM” and “WE + BBO-KSVM”. Conclusion: The proposed methods are superior to other methods on pathological brain detection (p < 0.05).     

Optimum Control of a Certain Class of Servomechanism†

Linear control systems governed by the vector matrix differential equation x = A x + B u have been considered. It has been shown how to find the optimum control u so that the system, starting from an initial position x(0), is steered to a state specifying the first p coordinates of the system in time t _o fixed in advance, the values attained by the (n—p) coordinates being immaterial, where n is the dimension of the system. The optimization considered here is with regard to the norm of u supposed to belong to L _m E _r space.     

利用像素置换的自适应可逆信息隐藏

目的 像素置换作为一种可逆信息隐藏方式具有良好的抗灰度直方图隐写分析能力,但嵌入容量偏小一直是其缺陷。针对这一问题,提出了一种基于像素置换的自适应可逆信息隐藏算法。方法 首先,与传统2×2像素块结构相比构造了尺寸更小的像素对结构,使得载体图像可以被更稠密地分割,为嵌入容量的提升提供了基数条件。其次,提出适用于该新像素结构的可嵌像素对（EPP）筛选条件,避免嵌入过程引起图像质量大幅下降。之后,根据EPP的灰度趋势差异对其进行自适应预编码,提高Huffman编码压缩比,进一步提升算法嵌入容量。最终,通过像素置换嵌入信息。结果 与2×2像素块结构的非自适应图像隐写算法相比,在同样保证灰度直方图稳定性的情况下该算法的PSNR提高了32%左右,嵌入容量提高了95%以上。其中自适应性对嵌入容量提升的贡献极大。结论 本文算法同时具有抗灰度直方图隐写分析能力与高嵌入容量性的可逆信息隐藏。算法构造了更高效的可嵌单位,并且针对不同载体图像的特点对其可嵌区域进行差异化编码。实验结果表明,本文算法在具有更好的不可见性的同时,嵌入容量得到大幅提升。     

矢量量化压缩图像中的安全隐写方法

提出一种用于矢量量化压缩图像的安全数据隐藏方案.为降低数据嵌入引入的失真,以码字间的矢量均方差为优化指标,采用遗传算法实现码本的优化分割,并提出基于码本分割的数据嵌入算法.采用基于自适应算术熵解码的数据映射方法,实现了嵌入前后统计特性的保持.实验结果表明,所提出的算法在容量、失真水平和安全性方面具有较好的综合性能.     

具有超分辨率行为伪装效果的可逆图像隐藏

目的 图像隐藏已成为计算机视觉领域的一个重要课题,其目的是以难以察觉的方式将秘密图像隐藏在载体图像中,同时要求接收端能够恢复秘密图像。尽管该技术发展迅速,但目前的图像隐藏技术大多是从内容层面进行伪装,追求载密图像与载体图像的不可区分性。其实,图像隐藏的本质是对行为安全的追求,因此不仅可以在内容层面进行伪装,还可以在行为层面进行伪装。方法 本文从行为安全的角度出发,提出了一种基于超分辨率行为伪装的可逆图像隐藏方法。与传统的图像隐藏技术不同,本文首先将秘密图像可逆地隐藏到载体图像中,生成载密图像,然后通过可逆的超分辨率处理创建与普通超分辨率图像处理操作无法区分的伪装图像。最后,允许接收方从伪装图像中恢复秘密图像和载体图像。结果 在图像隐藏和超分辨率两个任务中,本文方法均取得了优异的结果。在相同的数据集下,测试结果显示恢复秘密图像的峰值信噪比(peak signal-to-noise ratio, PSNR)值达到47+dB,较对比方法提升了2%以上,结构相似度(structure similarity index measure, SSIM)值也达到0.99+,超分辨率图像与Bicubic、SRCNN(super-resolution convolutional neural network)方法的结果相比,峰值信噪比(PSNR)提升了2+dB,感知指数(perceptual index, PI)值降低了2.02+。结论 本文提出的图像隐藏框架利用可逆超分辨率处理操作实现了行为安全角度的图像隐藏,在容量、安全性和精度上都具有优势。