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Alternative TitleBlind Steganalysis in Images
Thesis Advisor王蕴红 ; 谭铁牛
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword信息隐藏 隐秘通信 信息安全 信息隐藏检测 Data Hiding Steganography Covert Communication Information Security Steganalysis
Abstract信息隐藏是一种在数字多媒体中隐藏隐秘信息的技术,与之对应的信息隐藏检测是指分析多媒体数据或者其他可用于信息隐藏的载体,检测隐秘信息的存在,从而阻断可疑的隐蔽通信渠道。近年来,随着数字多媒体和网络技术的飞速发展,信息安全问题日益迫切,信息隐藏检测也受到国内外学术界与企业界的广泛关注,但作为一个新的研究领域,信息隐藏检测仍有很多理论与技术问题需要解决。本文对图像中的盲信息隐藏检测进行了深入研究,主要工作如下: 1.提出了一种基于图像经验矩阵统计分析的盲信息隐藏检测方法,该方法基于对图像经验矩阵的统计分析而提取高阶统计矩作为特征,利用支持向量机训练分类器识别正常图像和含隐图像,实验结果表明该算法能够针对多数典型的信息隐藏方法进行高性能检测,其检测性能优于目前该领域的主流算法; 2.基于上述方法提出了一种多模型的信息隐藏检测框架,该框架将针对不同信息隐藏方法的分类器灵活组合在一起,并且对各个分类器的性能进行优化,从而获得能够灵活扩展和定制的高性能盲信息隐藏检测框架,同时本文还针对样本多样性问题进行了一些尝试性研究,这在本领域的研究中具有创新意义; 3.构建了实现我们所提出方法的两个演示系统:信息隐藏还原工具箱和信息隐藏检测系统演示平台。
Other AbstractData hiding aims to hide secret information in digital media. In contrast, steganalysis is to analyze multimedia data or other cover data that can be used for data hiding, and detect the presence of hidden messages, so as to block suspicious covert communications. In recent years, with the development of digital multimedia and network technology, information security has become an urgent problem, and steganalysis has received much attention both in theoretical and industrial fields. However, as a relatively new research field, steganalysis has many problems to be solved. In this paper, we made extensive research in blind steganalysis in images, and the contributions in this paper are as follows: 1.Proposed a novel steganalysis method based on statistical analysis of empirical matrix. This method extracts high-order moments as features based on statistical analysis of empirical matrix of image, and utilizes Support Vector Machine to train classifiers to discriminate normal images and stego images. Experiments show that this method can attack most typical data hiding schemes, and it performs better than existing methods in the art. 2.Based on the above method, we further proposed a multi-model based steganalysis framework which flexibly combines various classifiers aiming at different data hiding schemes together and optimizes each classifer. This framework can make blind steanalysis for large variety of images with high precision. Additionally, we made some tentative research for the diversity problem which has innovative sense in this field. 3.We developed two demo systems to implement the methods we’ve proposed: Data Hiding and Recovery Toolbox, and Steganalysis Demo Platform.
Other Identifier200428014628041
Document Type学位论文
Recommended Citation
GB/T 7714
陈小川. 图像中的信息隐藏检测[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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