An image adaptive, wavelet-based watermarking of digital images

In digital management, multimedia content and data can easily be used in an illegal way—being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario digital watermark techniques are emerging as a valid solution. In this paper, we describe an algorithm—called WM2.0—for an invisible watermark: private, strong, wavelet-based and developed for digital images protection and authenticity. Using discrete wavelet transform (DWT) is motivated by good time-frequency features and well-matching with human visual system directives. These two combined elements are important in building an invisible and robust watermark. WM2.0 works on a dual scheme: watermark embedding and watermark detection. The watermark is embedded into high frequency DWT components of a specific sub-image and it is calculated in correlation with the image features and statistic properties. Watermark detection applies a re-synchronization between the original and watermarked image. The correlation between the watermarked DWT coefficients and the watermark signal is calculated according to the Neyman–Pearson statistic criterion. Experimentation on a large set of different images has shown to be resistant against geometric, filtering and StirMark attacks with a low rate of false alarm.     

A three-stage unsupervised dimension reduction method for text clustering

Dimension reduction is a well-known pre-processing step in the text clustering to remove irrelevant, redundant and noisy features without sacrificing performance of the underlying algorithm. Dimension reduction methods are primarily classified as feature selection (FS) methods and feature extraction (FE) methods. Though FS methods are robust against irrelevant features, they occasionally fail to retain important information present in the original feature space. On the other hand, though FE methods reduce dimensions in the feature space without losing much information, they are significantly affected by the irrelevant features. The one-stage models, FS/FE methods, and the two-stage models, a combination of FS and FE methods proposed in the literature are not sufficient to fulfil all the above mentioned requirements of the dimension reduction. Therefore, we propose three-stage dimension reduction models to remove irrelevant, redundant and noisy features in the original feature space without loss of much valuable information. These models incorporates advantages of the FS and the FE methods to create a low dimension feature subspace. The experiments over three well-known benchmark text datasets of different characteristics show that the proposed three-stage models significantly improve performance of the clustering algorithm as measured by micro F-score, macro F-score, and total execution time.     

Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition

Digital watermarking is important for protecting the intellectual property of remote sensing images. Unlike watermarking in ordinary colour images, in colour remote sensing images, watermarking has an important requirement: robustness. In this paper, a robust nonblind watermarking scheme for colour remote sensing images, which considers both frequency and statistical pattern features, is constructed based on the quaternion wavelet transform (QWT) and tensor decomposition. Using the QWT, not only the abundant phase information can be used to preserve detailed host image features to improve the imperceptibility of the watermark, but also the frequency coefficients of the host image can provide a stable position to embed the watermark. To further strengthen the robustness, the global statistical feature structure acquired through the tensor Tucker decomposition is employed to distribute the watermark's energy among different colour bands. Because both the QWT frequency coefficients and the tensor decomposition global statistical feature structure are highly stable against external distortion, their integration yields the proposed scheme, which is robust to many image manipulations. A simulation experiment shows that our method can balance the trade‐off between imperceptibility and robustness and that it is more robust than the traditional QWT and discrete wavelet transform (DWT) methods under many different types of image manipulations.     

Applying robust multibit watermarks to digital images

The current work is focusing on the implementation of a robust multibit watermarking algorithm for digital images, which is based on an innovative spread spectrum technique analysis. The paper presents the watermark embedding and detection algorithms, which use both wavelets and the Discrete Cosine Transform and analyzes the arising issues.     

Invertible chaotic fragile watermarking for robust image authentication

Fragile watermarking is a popular method for image authentication. In such schemes, a fragile signal that is sensitive to manipulations is embedded in the image, so that it becomes undetectable after any modification of the original work. Most algorithms focus either on the ability to retrieve the original work after watermark detection (invertibility) or on detecting which image parts have been altered (localization). Furthermore, the majority of fragile watermarking schemes suffer from robustness flaws. We propose a new technique that combines localization and invertibility. Moreover, watermark dependency on the original image and the non-linear watermark embedding procedure guarantees that no malicious attacks will manage to create information leaks.     

A cryptographic watermarking technique for multimedia signals

Digital watermarking has been widely used in digital rights management and copyright protection. In this paper, new cryptographic watermark schemes are proposed. Compare to the existing watermarking techniques, our proposed watermark schemes combine both security and efficiency that none of the existing schemes can do. We first develop an algorithm to randomly generate the watermark indices based on the discrete logarithm problem (DLP) and the Fermat’s little theorem. Then we embed watermark signal into the host image in both time domain and frequency domain at the indices. Our security analysis and simulation demonstrate that our proposed schemes can achieve excellent transparency and robustness under the major security attacks and common signal degradations. The novel approaches provided in this paper are ideal for general purpose commercial digital media copyright protection.     

Bregman operator splitting with variable stepsize for total variation image reconstruction

This paper develops a Bregman operator splitting algorithm with variable stepsize (BOSVS) for solving problems of the form $\min\{\phi(Bu) +1/2\|Au-f\|_{2}^{2}\}$ , where ? may be nonsmooth. The original Bregman Operator Splitting (BOS) algorithm employed a fixed stepsize, while BOSVS uses a line search to achieve better efficiency. These schemes are applicable to total variation (TV)-based image reconstruction. The stepsize rule starts with a Barzilai-Borwein (BB) step, and increases the nominal step until a termination condition is satisfied. The stepsize rule is related to the scheme used in SpaRSA (Sparse Reconstruction by Separable Approximation). Global convergence of the proposed BOSVS algorithm to a solution of the optimization problem is established. BOSVS is compared with other operator splitting schemes using partially parallel magnetic resonance image reconstruction problems. The experimental results indicate that the proposed BOSVS algorithm is more efficient than the BOS algorithm and another split Bregman Barzilai-Borwein algorithm known as SBB.     

多重水印技术的模糊关系与应用

应用带参数整数小波变换,提出了一种多重水印技术。在图像中同时嵌入半透明数字水印和脆弱水印来达到良好的保护效果。通过构造模糊关系矩阵,实现了半透明水印的安全嵌入。引入R ijndae l加密算法,构造出H ash函数,实现了一种具有高敏感性能的脆弱水印。该技术在图像保护领域有广阔的应用前景。     

Analysis and improvement of a chaos-based image encryption algorithm

The security of digital image attracts much attention recently. In Guan et al. [Guan Z, Huang F, Guan W. Chaos-based image encryption algorithm. Phys Lett A 2005; 346: 153–7.], a chaos-based image encryption algorithm has been proposed. In this paper, the cause of potential flaws in the original algorithm is analyzed in detail, and then the corresponding enhancement measures are proposed. Both theoretical analysis and computer simulation indicate that the improved algorithm can overcome these flaws and maintain all the merits of the original one.     

Functional data analysis for cash flow and transactions intensity continuous-time prediction using Hilbert-valued autoregressive processes

In this paper we use a functional autoregressive model as a robust predictor of the cash flow and intensity of transactions in a credit card payment systems. Intraday economic time series are treated as random continuous functions projected onto low dimensional subspace. Wavelet bases are considered for data smoothing. We compare two linear wavelet methods for the prediction problem of a continuous-time stochastic process on an entire time interval. Ex poste prediction is used to check the models.     

The use of a refined error bound when updating eigenvalues of tridiagonals

The Lanczos algorithm is used to compute some eigenvalues of a given symmetric matrix of large order. At each step of the Lanczos algorithm it is valuable to know which eigenvalues of the associated tridiagonal matrix have stabilized at eigenvalues of the given symmetric matrix. We present a robust algorithm which is fast (20j to 40j operations at the jth Lanczos step), uses about 30 words of extra storage, and has a fairly short program (approxiamately 200 executable statements)     

A novel digital image covert communication scheme based on generalized FCM in DCT domain

A novel covert communication method of digital image is presented, based on generalized fuzzy c-means clustering (GFCM), human visual system (HVS) and discrete cosine transform (DCT). Therefore, the original image blocks are classified into two classes according to specified characteristic parameters. So one block is suited for embedding security information, but the other block is not. Hence the appropriate blocks can be selected in an image to embed the security information by selectively modifying the middle-frequency part of the original image in conjunction with HVS and DCT. Furthermore the maximal information strength is fixed based to the frequency masking. Also to improve performances of the proposed algorithm, the security information is modulated into the chaotic modulation array. The simulation results show that we can remarkably extract the hiding security information and can achieve good robustness with common signal distortion or geometric distortion and the quality of the embedded image is guaranteed.     

模糊RBF神经网络在音频零水印中的应用

针对当前零水印不能"嵌入"有意义水印的不足,构建了在小波域中基于神经网络的零水印系统,提出了一种基于模糊RBF神经网络的音频零水印方案,有效解决了音频水印的鲁棒性与透明性之间的矛盾.模糊神经网络模糊系统的隶属度函数和推理规则决定RBF神经网络的结构和学习算法.因为水印方案不改变原始音频数据,所以具有良好的透明性,实验结果表明,方案具有很强的鲁棒性.     

Decision support for strategic energy planning: A robust optimization framework

Optimization models for long-term energy planning often feature many uncertain inputs, which can be handled using robust optimization. However, uncertainty is seldom accounted for in the energy planning practice, and robust optimization applications in this field normally consider only a few uncertain parameters. A reason for this gap between energy practice and stochastic modeling is that large-scale energy models often present features—such as multiplied uncertain parameters in the objective and many uncertainties in the constraints—which make it difficult to develop generalized and tractable robust formulations. In this paper, we address these limiting features to provide a complete robust optimization framework allowing the consideration of all uncertain parameters in energy models. We also introduce an original approach to make use of the obtained robust formulations for decision support and provide a case study of a national energy system for validation.     

Multiple-watermarking scheme based on improved chaotic maps

In this letter a new watermarking scheme for color image is proposed based on a family of the pair-coupled maps. Pair-coupled maps are employed to improve the security of watermarked image, and to encrypt the embedding position of the host image. Another map is also used to determine the pixel bit of host image for the watermark embedding. The purpose of this algorithm is to improve the shortcoming of watermarking such as small key space and low security. Due to the sensitivity to the initial conditions of the introduced pair-coupled maps, the security of the scheme is greatly improved.     

A robust chaotic algorithm for digital image steganography

This paper proposes a new robust chaotic algorithm for digital image steganography based on a 3-dimensional chaotic cat map and lifted discrete wavelet transforms. The irregular outputs of the cat map are used to embed a secret message in a digital cover image. Discrete wavelet transforms are used to provide robustness. Sweldens’ lifting scheme is applied to ensure integer-to-integer transforms, thus improving the robustness of the algorithm. The suggested scheme is fast, efficient and flexible. Empirical results are presented to showcase the satisfactory performance of our proposed steganographic scheme in terms of its effectiveness (imperceptibility and security) and feasibility. Comparison with some existing transform domain steganographic schemes is also presented.     

修正的TV-Stokes模型的新快速算法

基于Tai等人的前期工作,本文研究修正的TV-Stokes图像去噪模型,提出一些新的求解该两步模型的快速算法.我们利用对偶形式和多重网格方法得到一个求解第1步的快速算法.给出另外一种新的求解光滑的切向量场的保不可压性质的算法.在第2步中,我们提出一类有效的全新算法:首先通过计算Poisson方程得到具有光滑法向量场的函数g,然后利用Jia和Zhao的方法得到恢复的图像.新算法的运算速度非常快,用于图像恢复的CPU时间少于0.1 s.数值结果显示新的快速算法是有效的和稳定的,恢复图像的质量也超过了一般去噪方法.     

A two-phase decision support framework for the automatic screening of digital fundus images

In this paper we give a brief review on the present status of automated detection systems describe for the screening of diabetic retinopathy. We further detail an enhanced detection procedure that consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on the severity of abnormalities. If an image is found to be seriously abnormal, it will not be analysed further with robust lesion detector algorithms. As a further improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions of interest with possible lesions on the images that previously passed the pre-screening step. These regions will serve as input to the specific lesion detectors for detailed analysis. This procedure can increase the computational performance of a screening system. Experimental results show that both two steps of the proposed approach are capable to efficiently exclude a large amount of data from further processing, thus, to decrease the computational burden of the automatic screening system.     

A Recursive Algorithm to Restore Images Based on Robust Estimation of NSHP Autoregressive Models

The objective of this article is to present a new image restoration algorithm. First, each pixel in the image is classified into k categories. Then we assume that the gray levels in each category follow a nonsymmetric half-plane (NSHP) autoregressive model. Robust estimation of the parameters of the model is considered to attenuate the effect of the image contamination on the parameters. In each iteration we will construct a new image using a robustified version of the residuals. The introduction of the classification techniques as a first step of the algorithm reduces considerably the number of parameters to estimate. Hence, the computational time is also reduced because the robust estimations of the parameters are solutions of nonlinear system of equations. Some applications are presented to real synthetic aperture radar (SAR) images to illustrate how our algorithm restores an image in practice.     

A novel enhancement method for low illumination images based on microarray camera

It is difficult but important to get clear information from the low illumination im-ages. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the diff erent areas of an image with contrast brightness diff erences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we gen-eralize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well. Experimental results show that the proposed method can get better image enhancement eff ect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well. The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.