面向对象的中文PDF阅读器的设计与实现

本文介绍了面向对象的中文ＰＤＦ（ＰｏｒｔａｂｌｅＤｏｃｕｍｅｎｔＦｏｍａｎｔ）阅读器的设计与实现原理,首先对ＰＤＦ进行了简单介绍并指出开发中文ＰＤＦ阅读器的必要性,其次介绍了ＰＤＦ的结构并提出与之相适应的阅读器设计及实现方案。     

对PDF文档的认识与应用探讨

在当今的计算机世界里,Microsoft Word的Doc格式和Adobe Acrobat的PDF格式是使用率最高的两种文档格式,其中PDF是Internet上进行电子文档发行和数字化传播的理想格式。文章介绍了一些日常使用PDF文档的体会,希望有助于读者更好地应用PDF文档。     

Substituting outline fonts for bitmap fonts in archived PDF files

As collections of archived digital documents continue to grow the maintenance of an archive, and the quality of reproduction from the archived format, become important long‐term considerations. In particular, Adobe's portable document format (PDF) is now an important ‘final form’ standard for archiving and distributing electronic versions of technical documents. It is important that all embedded images in the PDF, and any fonts used for text rendering, should at the very minimum be easily readable on screen. Unfortunately, because PDF is based on PostScript technology, it allows the embedding of bitmap fonts in Adobe Type 3 format as well as higher‐quality outline fonts in TrueType or Adobe Type 1 formats. Bitmap fonts do not generally perform well when they are scaled and rendered on low‐resolution devices such as workstation screens. The work described here investigates how a plug‐in to Adobe Acrobat enables bitmap fonts to be substituted by corresponding outline fonts using a checksum matching technique against a canonical set of bitmap fonts, as originally distributed. The target documents for our initial investigations are those PDF files produced by LATEX systems when set up in a default (bitmap font) configuration. For all bitmap fonts where recognition exceeds a certain confidence threshold replacement fonts in Adobe Type 1 (outline) format can be substituted with consequent improvements in file size, screen display quality and rendering speed. The accuracy of font recognition is discussed together with the prospects of extending these methods to bitmap‐font PDF files from sources other than LATEX. Copyright © 2003 John Wiley & Sons, Ltd.     

基于PDF文档作为掩体的信息隐写方法

日前应用极为广泛的PDF文档,发现了其中存在可以用作信息隐写的隐密信道。通过采用以一定的冗余换取安全性的策略,并使用基于混沌模型的随机选择隐写单元的方法,使隐写系统满足Kerckhoffs原理。分析和实验结果表明,该文隐写方法可嵌入任意大小的信息,并保持在PDF阅读器中显示的透明性,具有较好的简单实用性。     

Weak Password Security: An Empirical Study

ABSTRACT

Portable Document Format (PDF) is a file format developed by Adobe Systems for representing documents in a manner that is independent of the original application software, hardware, and operating system. A PDF file can describe documents containing any combination of text, graphics, and images in a device independent and/or resolution independent format. It is now used everywhere across academic, technical, and governmental institutions. For secure communication, this platform and hardware independent file can be used as a secure cover media for voluminous data concealing. The methods develop so far for data hiding in PDF files satisfy low-embedding characteristics. In this work, we have developed two techniques. The first searches trash spaces in the PDF file and replaces them with the encrypted secret data. The second technique is useful for storing voluminous encrypted secret data without disturbing any format, features, or characteristics of the file. The practical implementation of this has been done on Microsoft platform. Experimental results show the feasibility of the proposed techniques.     

数字图书馆中信息采集子系统的设计与实现

电子文档的信息提取是建立数字图书馆的基础。论文主要介绍一个数字图书馆的信息采集子系统。它以PDF文件为提取对象,由全自动化录入和半自动录入两部分构成。全自动录入融合了基于规则和自动机两种提取方法的优点,具有速度快、准确率高等特点;半自动录入采用在AdobeAcrobat内部加入plug-in(插件)的方法为用户提供友好的使用界面,使用户可以方便地进行手工录入。     

PDF文件链接信息提取与修改

主要介绍了航空规章采用PDF文件格式,使用了书签、链接功能;利用AdobeAcrobat提供的编程接口编写Plug-in插件,实现了文件内和文件间的链接信息自动生成技术。     

在.NET平台上应用COM进行文档网上发布

人们经常利用办公软件制作各种文档,某些情况下需要将制作的办公文档在网上发布.为了能够批量地将办公文档发布到网页上,提出一种应用COM组件接口将文档转化为PDF格式文件,然后再将PDF文件转化为图片,把文档发布到网上的方法,使得信息的利用率大大提高,展示更容易.方法的实用性和扩展性强,具有批量处理文档的能力,能极大地提高工作效率,具有较好的应用价值.     

Non-Parametric Mixture Model Based Evolution of Level Sets and Application to Medical Images

We present a novel region-based curve evolution algorithm which has three primary contributions: (i) non-parametric estimation of probability distributions using the recently developed NP windows method; (ii) an inequality-constrained least squares method to model the image histogram with a mixture of nonparametric probability distributions; and (iii) accommodation of the partial volume effect, which is primarily due to low resolution images, and which often poses a significant challenge in medical image analysis (our primary application area). We first approximate the image intensity histogram as a mixture of non-parametric probability density functions (PDFs), justifying its use with respect to medical image analysis. The individual densities in the mixture are estimated using the recent NP windows PDF estimation method, which builds a continuous representation of discrete signals. A Bayesian framework is then formulated in which likelihood probabilities are given by the non-parametric PDFs and prior probabilities are calculated using an inequality constrained least squares method. The non-parametric PDFs are then learnt and the segmentation solution is spatially regularised using a level sets framework. The log ratio of the posterior probabilities is used to drive the level set evolution. As background to our approach, we recall related developments in level set methods. Results are presented for a set of synthetic and natural images as well as simulated and real medical images of various anatomical organs. Results on a range of images show the effectiveness of the proposed algorithm.     

基于机器学习算法的恶意PDF检测模型

随着互联网的高速发展和办公自动化的日益普及,PDF（portable document format）文件已经成为全球电子文档分发的开放式标准,由于PDF文档的高实用性和普遍适应性,使其成为有针对性钓鱼攻击的有效载体。恶意代码对计算机的严重破坏性,检测和防止含有恶意代码的PDF文档已日益成为计算机安全领域的重要目标。通过从文档中提取特征数据,提出了一个基于机器学习算法的恶意PDF检测框架,最后并通过实验验证了其检测模型的有效性。     

基于过滤驱动的透明加密文件系统模型

传统加密软件在实际应用中一般要求用户在使用前手动解密文件,使用结束后再手动加密文件,整个过程十分繁琐,且文件在操作过程中以明文形式存储在硬盘上,存在安全隐患。该文针对传统加密软件的不足,采用过滤驱动技术,设计一个透明加密的文件系统模型,并予以实现。该模型无需用户改变使用习惯,全程实时透明加密数据,有效保障了数据的安全性,提高了工作效率,改善了用户体验。     

Directionlet-based method using the Gaussian mixture prior to SAR image despeckling

In this article, a new denoising algorithm is proposed based on the directionlet transform and the maximum a posteriori (MAP) estimation. The detailed directionlet coefficients of the logarithmically transformed noise-free image are considered to be Gaussian mixture probability density functions (PDFs) with zero means, and the speckle noise in the directionlet domain is modelled as additive noise with a Gaussian distribution. Then, we develop a Bayesian MAP estimator using these assumed prior distributions. Because the estimator that is the solution of the MAP equation is a function of the parameters of the assumed mixture PDF models, the expectation-maximization (EM) algorithm is also utilized to estimate the parameters, including weight factors and variances. Finally, the noise-free SAR image is restored from the estimated coefficients yielded by the MAP estimator. Experimental results show that the directionlet-based MAP method can be successfully applied to images and real synthetic aperture radar images to denoise speckle.     

Proactive defense against malicious documents: formalization,implementation and case studies

The current detection model used by modern antivirus software is based on the same basic principle. Any antivirus has to analyze the threat in order to protect the user afterwards. This implies to have first a few systems to be infected, then to perform a manual or partially automated analysis of the malware to finally update the malware databases. Quite no prevention model is considered to mitigate this inherent limitation of AV software. This issue becomes critical when considering office documents (Microsoft Office, Libre Office, PDF files\(\ldots \)) which become more and more vectors of targeted attacks and hence represent a major threat. The huge variability of documents makes the current detection model quite useless. To protect against the specific risks presented by these documents, we propose a new model of antiviral protection acting proactively and offering a strong prevention model. The document is transformed into an inactive file format to protect the user from any known or unknown threat. This module of proactive threat management has been implemented into the DAVFI project (French and International AntiVirus Demonstrator), funded by the French Strategic Digital Fund. Real and concrete cases of malicious office documents have been submitted to the analysis of this module as well as its transformation principles, demonstrating its effectiveness and accuracy.     

Text-independent writer identification and verification using textural and allographic features

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates     

将Word文档自动转换成PDF格式的编程实现

在日常工作中,人们普遍使用微软Word编辑器制作文本文档,而PDF格式文档以其平台独立性和安全性等特点备受文档管理和发布者青睐。本文介绍Word文档到PDF文档转换的手工操作方法,同时详细论述编制转换组件,建立不同应用环境下的应用程序的过程,实现MS Word格式文档向PDF格式文档的自动、批量转换。     

恶意PDF文档检测技术研究进展*

随着PDF文档使用的日益普及,针对PDF的漏洞及相应攻击手段日渐增多,为政府、企业、重要组织等的网络办公安全产生了极大的威胁,恶意PDF文档检测技术逐渐成为近年来网络安全研究的热点。目前针对恶意PDF文档检测的研究已取得一定成果,为了更深入地解决该技术存在的不足。采用文献分析方法,首先讨论了必要性、简述了其相关概念和检测基本框架。其次针对其分析技术的不同,将现有方案进行分类,从适用范围、检测效果、检测效率等多个方面进行对比分析。最后归纳了该领域当前的热点和发展前景,以期为下一步研究提供参考。     

提高wbStego4性能的方法及其实现

wbStego4是目前公开的PDF文档隐写工具。该文分析并改进了wbStego4,使隐写算法可以在PDF文档中嵌入任意大小的数据,并保持在PDF阅读器中显示的透明性;通过采用以一定的冗余换取安全性的策略,并使用基于混沌模型的随机选择隐写单元的方法,使隐写系统满足Kerckhoffs原理。分析和实验结果表明了该文算法提高了wbStego4的隐写容量和安全性,使隐写算法具有更好的实用性。     

基于MGC法的非线性随机系统PDF控制

对于非线性随机系统,以均值、方差等低阶统计特征作为研究目标往往难以满足实际的控制要求,需要考虑更高阶的统计特征.概率密度函数(Probability density function,PDF)包含了完全统计特征,因此PDF控制能够实现各阶矩的有效控制.针对受高斯白噪声激励的非线性随机系统,将福克-普朗克-柯尔莫哥洛夫(Fokker-Planck-Kolmogrov,FPK)方程作为研究工具,提出一种基于多高斯闭合法(MGC)的PDF控制方法.首先,根据目标PDF的形状构造一个由多个高斯型PDF相叠加的PDF;然后,构造一个优化问题,使得该PDF逼近目标PDF;进一步,通过求解FPK方程得到被控系统的状态方程;最后,结合原始状态方程求得控制函数,实现对目标PDF的追踪控制.针对不同类型目标PDF进行的仿真结果表明了所提出方法的可行性和有效性.     

面向恶意PDF文档分类的对抗样本生成方法研究CSCD

通过恶意文档来传播恶意软件在现代互联网中是非常普遍的,这也是众多机构面临的最高风险之一。PDF文档是全世界应用最广泛的文档类型,因此由其引发的攻击数不胜数。使用机器学习方法对恶意文档进行检测是流行且有效的途径,在面对攻击者精心设计的样本时,机器学习分类器的鲁棒性有可能暴露一定的问题。在计算机视觉领域中,对抗性学习已经在许多场景下被证明是一种有效的提升分类器鲁棒性的方法。对于恶意文档检测而言,我们仍然缺少一种用于针对各种攻击场景生成对抗样本的综合性方法。在本文中,我们介绍了PDF文件格式的基础知识,以及有效的恶意PDF文档检测器和对抗样本生成技术。我们提出了一种恶意文档检测领域的对抗性学习模型来生成对抗样本,并使用生成的对抗样本研究了多检测器假设场景的检测效果(及逃避有效性)。该模型的关键操作为关联特征提取和特征修改,其中关联特征提取用于找到不同特征空间之间的关联,特征修改用于维持样本的稳定性。最后攻击算法利用基于动量迭代梯度的思想来提高生成对抗样本的成功率和效率。我们结合一些具有信服力的数据集,严格设置了实验环境和指标,之后进行了对抗样本攻击和鲁棒性提升测试。实验结果证明,该模型可以保持较高的对抗样本生成率和攻击成功率。此外,该模型可以应用于其他恶意软件检测器,并有助于检测器鲁棒性的优化。     

面向恶意PDF文档分类的对抗样本生成方法研究

通过恶意文档来传播恶意软件在现代互联网中是非常普遍的,这也是众多机构面临的最高风险之一。PDF文档是全世界应用最广泛的文档类型,因此由其引发的攻击数不胜数。使用机器学习方法对恶意文档进行检测是流行且有效的途径,在面对攻击者精心设计的样本时,机器学习分类器的鲁棒性有可能暴露一定的问题。在计算机视觉领域中,对抗性学习已经在许多场景下被证明是一种有效的提升分类器鲁棒性的方法。对于恶意文档检测而言,我们仍然缺少一种用于针对各种攻击场景生成对抗样本的综合性方法。在本文中,我们介绍了PDF文件格式的基础知识,以及有效的恶意PDF文档检测器和对抗样本生成技术。我们提出了一种恶意文档检测领域的对抗性学习模型来生成对抗样本,并使用生成的对抗样本研究了多检测器假设场景的检测效果(及逃避有效性)。该模型的关键操作为关联特征提取和特征修改,其中关联特征提取用于找到不同特征空间之间的关联,特征修改用于维持样本的稳定性。最后攻击算法利用基于动量迭代梯度的思想来提高生成对抗样本的成功率和效率。我们结合一些具有信服力的数据集,严格设置了实验环境和指标,之后进行了对抗样本攻击和鲁棒性提升测试。实验结果证明,该模型可以保持较高的对抗样本生成率和攻击成功率。此外,该模型可以应用于其他恶意软件检测器,并有助于检测器鲁棒性的优化。