基于马尔可夫模型和特征融合的图像隐写分析

提出一种针对JPEG图像隐写的通用隐写分析方法.根据量化后分块DCT系数绝对值构造水平、垂直和zigzag方向的差分数组,利用三向差分数组马尔可夫模型挖掘量化后分块DCT块内邻近系数相关性,提取转移概率矩阵的特征.对三向特征加权融合后进行隐写分析,以提高分类性能.对安全性较高的JPEG隐写OutGuess和F5,在不同嵌入率下进行隐写分析.实验结果显示,引入特征融合后隐写分析的检出率明显提高.     

基于连续隐马尔可夫模型的人脸识别方法

提出了一种基于连续隐马尔可夫模型的人脸图像识别方法,主要内容包括以下方面:①由于奇异值向量具有稳定性.转置不变性等特点,对归一化的人脸图像,采用奇异值分解抽取人脸图像特征作为观察值序列;②在人脸识别中应用连续隐马尔可夫模型,采用双高斯概率密度函数训练,建立HMM模型,再利用建好的HMM模型进行识别.实验结果显示,所提出的方法减少了数据计算量,运行速度快,并提高了识别率,完全满足人脸识别系统实时性要求.     

基于隐马尔可夫模型的网络控制系统延时状态估计

网络控制系统(NCS)中存在不确定的网络延时,使网络控制系统的分析和设计十分困难;针对这一问题,采用隐马尔可夫模型(HMM)的隐含层状态估计算法,以校园网络为实验平台,通过观测到的网络控制系统输出信号来获得估计信号,从而间接地对被随机时延噪声污染而存在顺序混乱的状态信号进行估计;并对不同时间间隔条件下,直接状态估计和扩展空问状态估计这两种方法的适用性进行了分析比较;实验结果表明在中小型网络控制系统中,基于隐马尔可夫模型的时延估计方法是简单、有效的.     

基于混合因子分析的隐马尔可夫模型

经典隐马尔可夫模型用于语音识别存在的两个主要缺陷是“离散状态假设”和“独立分布假设”。前者忽略了语音信号的非平稳性,后者忽略了语音信号的相关性。文章将混合因子分析方法用于语音建模,提出了基于混合因子分析的隐马尔可夫模型框架,并用动态贝叶斯网络形象地表示。该模型框架不仅从理论上解决了上述问题,而且给出许多语音建模的选择。目前广泛使用的统计声学模型均可视为该模型的特例。     

基于Contourlet域的隐马尔可夫树模型的图像融合算法

提出一种基于Contourlet域隐马尔可夫树(CHMT)的多聚焦图像融合方法。CHMT能有效捕获不同尺度系数之间、不同方向系数之间的相关性,能为图像融合提取更多的特征信息。算法对低频子带采用区域方差法,高频子带则依据训练后模型的每一系数的后验概率进行不同的融合处理,以减少融合图像边缘处的斑块模糊现象。仿真实验结果表明,该算法优于基于Contourlet域的常规融合算法,融合后的图像具有更好的主观视觉效果。     

基于隐马尔可夫模型的查询扩展方法

对查询进行扩展的目的是找出查询中的潜在语义,确定用户意图,进而构造更适合于搜索引擎检索的查询语句,以提高检索的准确率。提出利用隐马尔可夫模型预测查询中的潜在语义的方法,该模型在大规模用户查询日志上进行训练。由该模型预测出的扩展语句查询的准确率较词共现扩展、同义词扩展等方案均有明显提升。     

训练隐马尔可夫模型的BW-GA方法

Baum-Welch算法是训练HMMs的传统方法,该方法虽然收敛速度快,但容易陷入局部最优,影响了序列比对的质量。针对该算法存在的问题,结合生物遗传与进化的规律,设计了一种将传统方法与遗传算法相结合训练HMMs的BW-GA方法。根据序列比对的需要和HMMs的结构,定义了3种遗传操作和编码方式。用19条原核5sRNA序列对模型进行了训练,用BW-GA训练模型产生序列的对数似然概率比单独用传统方法训练的要高,产生序列比对的质量较好。     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

基于禁止搜索的离散隐马尔可夫模型优化

隐马尔可夫模型(HMM,Hdden Markov Model)是语音识别中广泛采用的鲁棒性统计方法.本文采用禁止搜索(TS,Tabu Search)算法训练HMM参数,提出了基于禁止搜索的隐马尔可夫模型(TS-HMM)算法.该算法可以使搜索最优模型参数的过程达到全局优化.仿真结果表明与传统的前向-后向算法相比,TS-HMM算法具有更好的性能,且能够达到全局优化.     

Hidden Markov Models for Software Piracy Detection

Abstract

In this paper, we analyze a method for detecting software piracy. A metamorphic generator is used to create morphed copies of a base piece of software. A hidden Markov model is trained on the opcode sequences extracted from these morphed copies and the resulting trained model is used to score suspect software to determine its similarity to the base software. A high score indicates that the suspect software may be a modified version of the base software, suggesting that further investigation is warranted. In contrast, a low score indicates that the suspect software differs significantly from the base software. We show that our approach is robust, in the sense that the base software must be extensively modified before it is not detected.     

计算机取证技术的研究

计算机取证已成为网络安全领域研究的一大热点。该文给出了计算机取证的概念和一般原则,详细描述了计算机取证的步骤和关键技术,分析了目前计算机取证存在的不足和反取证技术,最后给出了发展趋势。     

支持计算机取证的电子证据安全保护策略

计算机数据具有脆弱性,因此必须在计算机取证过程中对电子证据进行安全保护使其具有证据能力。该文提出一种基于SHA-1, RSA和Rabin容错算法实现的电子证据安全保护策略以支持计算机取证,对原始记录进行实时、安全的转移并分散存储,保证数据的真实性和完整性,能通过快速检测和还原对其进行有效性鉴定。     

基于隐马尔可夫模型的火焰检测

提出一种利用隐马尔可夫模型对普通视频中的火焰进行分析的方法,除应用运动和颜色分析对火焰进行识别外,还通过隐马尔可夫模型对火焰的闪烁特性进行分析。实验结果表明,该方法能有效区分火焰和具有火焰颜色的普通运动物体,减少了火灾监测中误报警的次数,具有一定的实际意义。     

计算机取证技术的研究

计算机取证已成为网络安全领域研究的一大热点。该文给出了计算机取证的概念和一般原则．详细描述了计算机取证的步骤和关键技术,分析了目前计算机取证存在的不足和反取证技术,最后给出了发展趋势。     

Symbolic Place Recognition in Voronoi-Based Maps by Using Hidden Markov Models

This article presents a new algorithm to recognize natural distinctive places such as corridors, halls, narrowings, corridors with doors opening on the left side, etc., from indoor environments using Hidden Markov Models (HMM). HMM give a stochastic solution which can be used to make decisions on localization, navigation and path-planning. The environment is modeled as a topo-geometric map which combines topological and geometric information. This map is obtained from a Voronoi diagram using measurements of a laser telemeter. The characteristics of topo-geometric map (nodes, number of edges adjacent to nodes, slope of edges, etc.) are used to learn and to recognize the different places typical of indoor environments. This map can be used in order to resolve several problems in robotics such as localization, navigation and path-planning. Our method of place recognition is a fast and effective way for a robot to recognize typical places of indoor environments from a topo-geometric map.     

Hidden Markov Models for Text Categorization in Multi-Page Documents

In the traditional setting, text categorization is formulated as a concept learning problem where each instance is a single isolated document. However, this perspective is not appropriate in the case of many digital libraries that offer as contents scanned and optically read books or magazines. In this paper, we propose a more general formulation of text categorization, allowing documents to be organized as sequences of pages. We introduce a novel hybrid system specifically designed for multi-page text documents. The architecture relies on hidden Markov models whose emissions are bag-of-words resulting from a multinomial word event model, as in the generative portion of the Naive Bayes classifier. The rationale behind our proposal is that taking into account contextual information provided by the whole page sequence can help disambiguation and improves single page classification accuracy. Our results on two datasets of scanned journals from the Making of America collection confirm the importance of using whole page sequences. The empirical evaluation indicates that the error rate (as obtained by running the Naive Bayes classifier on isolated pages) can be significantly reduced if contextual information is incorporated.     

云模式下网络犯罪侦查与取证研究

本文针对云计算技术在取证分析领域的应用进行研究探讨,以期将现有的电子物证实验室从单一功能转变为多功能的、支持远程受理、远程鉴定的、资源整合型的＂云模式取证实验室＂,从而从根本上改变传统计算机取证技术与应用的被动局面。     

Factorial Hidden Markov Models

Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach's chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot.