An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

应用于ROHC的窗基LSB编解码修正算法实现机制

在移动：IPv6中，常常因为数据分组的头标太大而消耗过多的带宽，因而需要使用灵活可靠的鲁棒性头标压缩机制(ROHC)来对分组头标进行压缩。在ROHC中，使用LSB编码方案对分组中那些变化比较小的头标域进行编码。本文将在简要介绍ROHC原理的基础上，深入探讨应用于ROHC的窗基LSB编解码修正算法的实现机制。     

On parity problems and the functional-link artificial neural network

This paper contains the proof of a theorem on the capability of functional-link artificial neural networks both to represent and to learn the n-dimensional parity problem. The result is obtained by an embedding of the problem into a space of dimension 2ⁿ — 1. It is shown that the Volterra expansion of the data in n-dimensions provides the necessary transformation. By computing the parity function, it is shown that a suitable set of neural network weights can be deduced. Finally, it is demonstrated that 2ⁿ — 1 is the minimum embedding dimension for the problem.The contribution of A Zuderell of the University of Innsbruck is acknowledged.     

Micromechanical modelling of ductile crack growth in the binder phase of WC–Co

This study focuses on numerical simulation of ductile failure in the Co binder phase of WC–Co hardmetal. The growth of edge cracks under mode I loading is considered. A computational micromechanics approach is taken where the Co binder ligaments are explicitly represented in finite element models. An embedding technique is employed. Crystal plasticity theory is used to represent plastic deformation in the Co ligaments. Crack propagation in the binder is simulated using an element removal technique based on a modified Rice and Tracey model for ductile void growth, and fracture resistance curves are generated. Parameter studies are performed for variations in microstructrual parameters such as numbers of Co ligaments ahead of the crack tip and local Co volume fraction. The importance of thermal residual stresses and finite element mesh density are also investigated.     

Optimal control of nonlinear systems to given orbits

Using optimal control techniques we derive and demonstrate the use of an explicit single-step control method for directing a nonlinear system to a target orbit and keeping it there. We require that control values remain near the uncontrolled settings. The full nonlinearity of the problem in state space variables is retained. The “one-step” of the control is typically a composition of known or learned maps over (a) the time required to learn the state, (b) the time to compute the control and (c) the time to apply the control. No special targeting is required, yet the time to control is quite rapid. Working with the dynamics of a well-studied nonlinear electrical circuit, we show how this method works efficiently and accurately in two situations: when the known circuit equations are used, and when control is performed only on a Poincaré section of the reconstructed phase space. In each case, because the control rule is known analytically, the control strategy is computationally efficient while retaining high accuracy. The target locations on the selected target trajectory at each control stage are determined dynamically by the initial conditions and the system dynamics, and the target trajectory is an approximation to an unstable periodic orbit of the uncontrolled system. A linear stability analysis shows that dissipation in the dynamical system is essential for reaching a controllable state.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

基于组合方法的人脸识别算法研究

该文试探一种加权融合流形学习的方法进行人脸识别,该算法通过Haar小波和局部线性嵌入（LLE）加权融合的方式来进行人脸识别。首先通过Haar小波变换对人脸图像进行一级分解,得到四个子图;然后利用LLE算法对四个子图降维处理,并加权融合;最后利用支持向量机（SVM）进行分类判决。通过在ORL库中进行实验,结果表明,该文提出的Haar＋LLE识别效率比单独使用主成分分析（PCA）和LLE更高效。     

数字音频水印的有效性和可靠性研究

针对音频水印的LSB算法的可靠性较差的问题,运用纠错编码和随机序列来提高水印系统的可靠性,通过增多水印的嵌入位数来提高水印系统的有效性。实验结果表明,水印系统的可靠性和有效性都得到了明显的改善。