基于W2ID准则的RichModel隐写检测特征选取方法  

W2ID Criterion-Based Rich Model Steganalysis Features Selection

作　　者：马媛媛[1,2] 徐久成 张祎 杨春芳 罗向阳 MA Yuan-Yuan;XU Jiu-Cheng;ZHANG Yi;YANG Chun-Fang;LUO Xiang-Yang(Ienan Normal University,Xinriang,Ieran 453002;PLA Siralegic Supporl Force Information Engineering Universily,Zhengzhou 450001;Stale Key Laboratory of Maihematical Engineering and Adtanced Compuing,Zhengzhou 45000)

机构地区：[1]河南师范大学计算机与信息工程学院,河南新乡453002 [2]中国人民解放军战略支援部队信息工程大学,郑州450001 [3]数学工程与先进计算国家重点实验室,郑州450001

出　　处：《计算机学报》2021年第4期724-740,共17页Chinese Journal of Computers

基　　金：国家自然科学基金(U1804263,U1636219,61772549,1736214,61872448);国家重点研发计划(2016YFB0801303,2016QY01W0105);河南省科技创新杰出人才项目(184200510018);河南省科技攻关项目(202102210165)资助。

摘　　要：数字隐写是信息安全领域一个重要分支,其通过将秘密信息嵌入到数字图像、声音、视频等文件中并通过公开信道(如:Email邮箱、微博推文和即时通信等)进行传递,从而实现信息的隐蔽通信.图像自适应隐写是近年来数字隐写技术的研究热点,而Rich Model特征是检测图像自适应隐写的一大类主流高维特征,这类高维特征在实现对图像自适应隐写较高检测正确率的同时,带来了高额的计算开销和和存储开销,并使得隐写检测中的分类器训练变得极为困难.为此,本文提出了一种基于加权类间距离和类内距离差异准则(W2ID准则)的图像Rich Model隐写检测特征选取方法(记为W2ID-α方法).首先,在对Fisher-based方法这一隐写检测特征经典选取方法进行原理分析的基础上,指出该方法可能存在误删有用特征分量、保留冗余和冲突特征分量的不足;然后,通过将"类内距离差异"原则引入到隐写检测特征分量的可分性度量,提出了基于类间距离和类内距离差异的特征可分性度量准则(简记为2ID准则),给出了类内距离差异的一个相关性质;同时,为了合理体现"类间距离"的重要性,本文提出了基于频数统计加权法的权重分配算法,为该准则分配合理权重,使得对特征分量可分性的度量结果相比传统的Fisher准则更为准确;最后,依据W2ID准则的度量结果,基于决策粗糙集α-正域约简方法约简隐写检测特征分量,并在约简特征分量过程中,将每次处理一个特征分量改进为每次处理一组特征分量,以提升决策粗糙集α-正域约简的效率.提出的W2ID-α方法因无需设置可分性下限,避免了阈值设置不准确可能造成去除有用特征分量的问题,从而消除了现有Steganalysis-α隐写检测特征选取方法依赖经验参数的问题.基于数字隐写领域通用的BOSSbase-1.01图像库10 000幅原始图像和基于经典SI-UNIWARD隐写方法生成的多组隐写�Digital steganography is an important branch of the information security technology.Digital steganography embeds the secret information into the digital files,which includes image,voice,video and others,and transmits them through public channels(e.g.E-mail,Twitter,Instant messaging,etc.),so as to realize transmitting the information in secret.The image adaptive steganography is a research hotspot in the area of steganography in recent years,while the Rich Model steganalysis feature is the mainstream high-dimensional feature for detecting image adaptive steganography.This kind of high dimension steganalysis feature not only achieves high detection accuracy of image adaptive steganography,but also brings the high computational overhead and storage space.Thus,the classifier in steganalysis is very difficult to train.For this reason,this paper proposes a steganalysis feature selection method(W2ID-a) based on the Weight Inter-class Distance and Intra-class distance Difference criterion(W2ID criterion).First,this paper analyzes the principle of the Fisher-based method,which is a classical steganalysis feature selection method.Then,it points out that this method may have the shortcomings of deleting useful feature components,retaining redundant and conflicting feature components by mistake.Second,by introducing the principle of "Intra-class distance Difference" to separability measurement of the steganalysis feature component,this paper proposes a separability measurement criterion based on both Interclasses Distance and Intra-class distance Difference(2ID criterion).The properties of Intra-class aggregation Difference are also given.Meanwhile,in order to highlighting the importance of the "Inter-class Distance",a weight assignment algorithm based on the frequency statistical weighting method is proposed to assign weights to the separability criterion reasonably.This criterion is called W2ID criterion.Thus,the separability result of the steganalysis feature component can be measured by the W2ID criterion more accurate t

关 键 词：隐写检测 RichModel 特征选取 W2ID准则 α-正域约简 Fisher-based方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]