P.W.Wong脆弱性水印算法

《信息隐藏技术》课程期末复习资料
《信息隐藏技术》课程讲稿章节目录：
第1章概论
什么是信息隐藏
信息隐藏的历史回顾
技术性的隐写术
语言学中的隐写术
分类和发展现状
伪装式保密通信
数字水印
信息隐藏算法性能指标
第2章基础知识
人类听觉特点
语音产生的过程及其声学特性
语音信号产生的数字模型
听觉系统和语音感知
语音信号的统计特性
语音的质量评价
人类视觉特点与图像质量评价
人类视觉特点
图像的质量评价
图像信号处理基础
图像的基本表示
常用图像处理方法
图像类型的相互转换
第3章信息隐藏基本原理
信息隐藏的概念
信息隐藏的分类
无密钥信息隐藏
私钥信息隐藏
公钥信息隐藏
信息隐藏的安全性
绝对安全性
秘密消息的检测
信息隐藏的鲁棒性
信息隐藏的通信模型
隐藏系统与通信系统的比较
信息隐藏通信模型分类
信息隐藏的应用
第4章音频信息隐藏
基本原理
音频信息隐藏
LSB音频隐藏算法
回声隐藏算法
简单扩频音频隐藏算法
扩展频谱技术。

21春学期（1709、1803、1809、1903、1909、2003、2009、2103）《信息隐藏技术》在线作业9.下列关于半脆弱水印的描述,不正确的是( )。

A:半脆弱水印是特殊的水印，它的稳健性介于鲁棒水印和脆弱水印之间，可以判定图像经受的是普通信号处理操作还是图像内容篡改操作。

B:LSB算法可作为半脆弱水印算法，对图像的操作，无论是否影响图像内容，都将导致该算法判定图像被篡改。

C:P. W. Wong水印系统是基于公钥图像认证和完整性数字水印系统，实质是脆弱水印系统。

D:一些半脆弱水印算法是由鲁棒水印算法演变来的。

答案：B57、()指的是水印与作品的使用工具相结合(如软硬件播放器等),使得盗版的作品无法使用。

A:用于拷贝保护的数字水印B:用于版权保护的数字水印C:用于盗版跟踪的数字指纹D:（A、B、C）都不是答案：A21.对二值图像可采用调整区域黑白像素比例的方法嵌入秘密信息。

确定两个阔值 R0<50%和R1>50%,以及一个稳健性参数λ。

隐藏1时,调整该块的黑色像素的比使之属于[[R1,R1+λ]; 隐藏0时,调整该块黑色像素的比例使之属于[R0一λ,R0]。

如果为了适应所嵌入的比特,目标块必须修改太多的像素,就把该块设为无效。

标识无效块: 将无效块中的像素进行少量的修改,使得其中黑色像素的比例大于R1+3λ,或者小于R0 -3λ。

则下列说法不正确的是( )。

A:稳健性参数λ越大，算法抵抗攻击的能力越强。

B:稳健性参数λ越大，算法引起的感官质量下降越小。

C:引入无效区间主要是为了保证算法的透明性。

D:算法所有参数都确定时，也不能准确计算一幅图像能隐藏多少比特信息。

1。

基于关系数据库的脆弱性水印算法研究
张立忠;姜楠;张洋
【期刊名称】《计算机工程与应用》
【年(卷),期】2008(44)29
【摘要】为了检测时关系数据库的恶意篡改,提出了一种脆弱性数字水印算法.该算法将数据库的元组划分到不同的分组中,在对每个分组内的元组进行秘密排序的基础上,生成由属性水印和元组水印构成的分组水印矩阵,因此可以将对数据库的篡改定位在分组范围内.利用单向哈希函数及关系数据动态生成水印,不但保证了水印信息的安全性,而且也实现了水印的盲检测.理论分析和实验结果表明,该方法能够有效探测攻击者对关系数据库进行元组添加、属性值修改、元组删除和属性变化四类操作,从而为关系数据的真实性认证提供依据.
【总页数】4页(P157-160)
【作者】张立忠;姜楠;张洋
【作者单位】沈阳化工学院,计算机科学与技术学院,沈阳,110142;沈阳化工学院,计算机科学与技术学院,沈阳,110142;沈阳化工学院,计算机科学与技术学院,沈
阳,110142
【正文语种】中文
【中图分类】TP309.2
【相关文献】
1.基于语音权重的关系数据库水印算法研究 [J], 高智贤;张业宏
2.一种基于关系数据库的脆弱性水印算法 [J], 杨大全;张立忠
3.一种基于关系数据库的脆弱性水印算法 [J], 杨大全;张立忠
4.基于遗传算法的关系数据库水印优化算法研究 [J], 王春芳;崔新春
5.基于循环冗余校验的关系数据库水印算法研究 [J], 孙亚鹏;廖俊国;刘晓加
因版权原因，仅展示原文概要，查看原文内容请购买。

收稿日期∶2007206223基金项目∶陕西省自然科学基金(2004F10)和陕西省教育厅专项基金(05J K 260)资助项目.作者简介∶郗艳华(19742),女,陕西蒲城人,硕士研究生,主要从事数字水印研究.文章编号∶100025862(2008)0120081205脆弱性数字文档水印技术郗艳华1,2,　张敏瑞1(1.西安科技大学通信与信息工程学院,陕西西安　710054;2.咸阳师范学院物理系,陕西咸阳　712000)摘要∶网络和信息技术的发展,使得大量的纸质文档可以以数字文档形式进行保存和传输,于是如何保证数字文档的真实性和完整性成为一个急需解决的问题.脆弱性文档水印技术的发展,为解决这一问题提供了重要途径.该文介绍脆弱性数字文档水印的基本概念和研究现状,重点分析脆弱性文档水印各种算法,比较其优缺点;最后指出了脆弱性数字文档水印技术今后的发展方向.关键词∶文档水印;脆弱水印;文档安全中图分类号∶TP 391.41 文献标识码∶A 计算机技术的发展为人们获取信息和进行信息交流带来了极大的方便,但也使恶意的个人或团体在没有得到授权的情况下对网络传输中有版权的数据文件或作品进行拷贝、篡改和传播.因此如何在网络环境中实施有效的版权保护与信息完整性检测已经成为一个迫切需要解决的问题.数字水印技术作为一种保护版权和检测信息完整性的有效方法,已成为信息安全领域的一个研究热点.特别是脆弱性文档水印技术的发展,为数字文档的真实性和完整性保护提供了一条重要的途径.本文首先介绍数字文档水印的基本概念,之后重点介绍和分析脆弱性文档水印技术的研究现状和现有算法的优缺点,最后指出脆弱性数字文档水印技术需要进一步研究的问题.1　数字文档水印的基本概念数字文档水印技术[1]是指在不影响数字文档内容的使用价值和欣赏价值的前提下,利用人体视觉系统的掩蔽特性,将与文档内容相关或不相关的一些标记信息(水印)直接嵌入在文档中.它可以用来证明创作者对其作品的所有权,也可以作为鉴定、起诉非法侵权的证据,同时还可以通过对水印的检测和分析来保证数字文档的完整性和可靠性.按照不同的分类标准,数字文档水印具有以下不同的分类方式:(1)根据实现方法的不同,可分为空间域数字水印和变换域数字水印[2]两大类.前者是在空间域中直接将水印信息嵌入到宿主文档中;后者是将待处理文档当作数字图像,先对该图像进行变换,然后在变换域中嵌入水印信息.(2)按照抗攻击性的不同,可分为鲁棒性水印、半脆弱性水印和脆弱性水印[223].半脆弱性水印适合验证文档内容的真实性,要求对一般性的处理(滤波,有损压缩等)表现出一定的鲁棒性,但对于篡改文档内容等操作具有很强的敏感性,能在一定程度上检验文档的完整性.脆弱性水印主要用于数字文档内容及版权等关键信息的真伪鉴定,防止非法篡改、伪造,保障数据的安全完整性.2　脆弱性文档水印的主要特性和系统框架2.1　脆弱性文档水印的主要特性脆弱性数字文档水印除了具备一般水印的不可见性和安全性[425]外,还应具有对篡改的高敏感性和对篡改检测的可靠性.其主要特性有:不可见性,安全性,脆弱性[4221],篡改检测,盲检测性.第32卷第1期2008年2月　江西师范大学学报(自然科学版)JOURNA L OF J I ANG XI NORM A L UNI VERSITY (NAT URA L SCIE NCE )V ol.32N o.1　Feb.20082.2　脆弱性文档水印的系统框架[4]脆弱性水印的嵌入与一般鲁棒性水印的嵌入原理基本是相同的.水印的嵌入模型如图1,检测模型如图2所示.在水印嵌入时,首先对原始文档进行特征提取,然后将提取出的文档特征(或原始水印)作为实际待嵌入的水印信息通过密钥嵌入原始文档中;在检测时,只需对待检测文档进行特征提取,然后根据相同的密钥,按照嵌入算法的逆方法来提取水印,并对待检测文档进行完整性检测.为了对篡改内容进行较好定位,有时还需要与原始水印进行比较.图1　脆弱性水印的嵌入模型图2　脆弱性水印的检测模型3　现有脆弱性文档水印算法从目前发表的文献来看,脆弱性文档水印几乎都是在空间域利用人眼的视觉特性修改单个像素或一组像素的值来嵌入水印信息的.像素点在修改时主要考虑两个方面的问题:一是如何选择要修改的像素点,尽量使得文档的改动不被觉察;二是如何构造修改像素与嵌入水印信息的关系,使得这种关系有利于盲检,有更好的安全性,更强的脆弱性.3.1　基于文档图像分块的水印算法Min Wu [8]等人于2000年提出根据人眼的视觉特性,通过考察修改某个像素后,它和邻域的8个像素的连通性与平滑性的变化,判断其是否可以修改;然后根据混洗表(排列图像中像素所用的某一规则)对图像进行混洗,将行与行或列与列的顺序完全打乱,并对图像进行分块,利用每块图像中黑色像素总数目的奇偶性来构造水印信息与图像像素之间的关系.该算法是一种非常有效的方法.首先,算法考虑到了人眼的视觉模型,提出了对像素可修改性进行判断的方法.文献[9]也利用此方法计算像素的可非度(是否可以修改),确定可嵌入水印的优先级,当对原始图像进行随机排列和分块后,将相同数量的水印嵌入在每一块中.其优点是能根据随机排列的统计特性自适应地确定可嵌入水印像素的数量.文献[10]提出将数字签名用于文献[8]的算法中,把文档图像内容分为两部分,一部分用来提取签名作为水印信息,另一部分用来嵌入水印信息.由于在算法中用来提取签名和嵌入签名的图像像素点都是随机选取的,所以提高了算法的抗攻击性,这种算法能够探测出很少比特甚至是一个比特像素的修改.文[8210]的算法是基于文档图像的每个块中黑像素数的奇偶特性而进行水印嵌入的,其最大的缺点是攻击者改动块中黑像素数目却不改变其奇偶性而通过检测.文[11]用一个加权矩阵求出该块的特征码,求出每个分块的特征码均值,将特征码均值作为索引建立一个内容签字的查询表,最后将内容签字和可翻动象素进行异或,若它们相互匹配,则不需要翻动象素,否则象素将被翻动.此方法对于检测恶意攻击非常有效.T zeng 等人提出一种算法[12],将要嵌入的随机序列每M 比特分为一组,将每一组嵌入在一个图像块中.嵌入时,首先在该块中随机选择若干个位置,将M 比特的水印信息与各位置处长度为M 的原始文档序列进行匹配运算,从中选择匹配结果最佳的一个序列,将其用水印比特替换.检测时,则根据能否提取出水印信息来判断图像的真伪.该算法中水印的不可见程度通常取决于所选用的块的大小,一般说来,图像块越大,越容易找到较为理想的嵌入位置.Wu 等[14]提出一种用于二值传真图像的数字水印算法,该算法随机选择一个m ×n 大小的二值矩阵作k 为密钥,将原始图像F 分成大小为m ×n 的块,对各块进行操作.设第i 块图像为Fi ,若0<SUM (F i ∧K )<SUM (K )(其中,“∧”为“与”操作符,SUM (K )表示对k 中各元素进行求和),则在该块嵌入一比特信息,否则,该块保持不变.在检测端,设嵌入比特为b ,被测图像块为F ′i ,若0<28江西师范大学学报(自然科学版)2008年SUM (F ′i ∧K )<SUM (K ),则b =SUM (F ′i ∧K )mod 2,由此便可提取出水印信息.这样,最多修改一个比特,该算法便可在的图像块中嵌入一比特信息.为进一步增加水印嵌入量,文献[15]对上述算法进行改进,用异或操作“ ”来取代与操作“∧”,采用加权矩阵w 来提高水印嵌入量,算法在一个m ×n 的图像块中最多修改两个比特,与前述算法相比,水印嵌入量有较大提高.但算法只着重于如何修改较少的比特来使增/减到需要值,并未考虑水印的不可视性.为此文献[16217]在上述算法基础上进行优化.文献[16]引入人眼视觉特性,对可修改的点集添加了视觉约束条件,即只对满足视觉条件的点进行修改.显然,水印的不可感知性虽然增强了,但能够嵌入的信息量却减少了.文献[17]增加了对像素可翻动性的考察,并且经过混洗使块中可翻动像素得到平均,它在保持含水印图像质量的同时保证嵌入的信息量不减少.3.2　基于文档图像中字(或词)分块的水印算法文献[8]的算法使用混洗表对图像进行置乱,这样可以使图像中可修改像素分布更为均匀,从而提高了图像整体嵌入水印的容量,而且可以实现盲检测.但是混洗带来了一些问题:(1)提取水印需要混洗表,传送信息时需要传送这一附加信息,而且要求通信双方将混洗表作为密钥保存,显然不实际;(2)混洗将图像分散置乱的同时也将篡改分散,所以无法通过提取的水印对篡改进行定位.基于此两点,王欣等[18]提出了一种针对汉字文档的算法,它省去了混洗以及混洗所带来的附加信息.原因是试验证明:在将由相等大小汉字组成的文档图像基于汉字大小分块时,混洗前后每块图像中可修改像素分布的均匀性并没有得到明显的改善,相反,某些时候甚至将原本比较均匀的分布变得不均匀.这种方法实际上是半脆弱性水印,其缺点是在检测时需要原始水印.文献[19]将文档图像按单词分块后,提取该单词块的外边缘点,将水印信息嵌入.算法只对纯英文文档有效,而文献[20]将文档按单词或字分块后,再将每一个块按其宽度分成一些正方形子块,取其对角线上的点和中间点,从中随机选取一部分点,以此来嵌入水印信息.算法对于二值中文和英文文档图像都有效.检测时,都是利用与嵌入时相逆的算法提取水印来判断文档是否被篡改.3.3　基于混沌理论的水印算法文献[21222]利用混沌序列对初值的高敏感性来增强水印的脆弱性.文献[21]先定义了最不重要像素块(Least Significant Pixel Block ,LSPB ):在以点p 为中心的3×3像素块B 中,点p 的像素值用v (p )表示,如果v (p )-v (p i )≠0,称pp i 为一条边界;块内所含边界数N e 为N e 3=68i =1v (p )-v (p i ).在由n 个3×3像素块组成的文档图像区域,如果改变块j (j =1,2,…,n )的中心像素值能使该块的边界数变化量最小,称块j (j =1,2,…,n )为该区域内的最不重要像素块.算法将原始文档图像分区域,区域中又分小块,利用混沌对初值敏感的特性,将块内那些不嵌入水印的点的像素值映射为混沌初值,并由混沌迭代终值生成水印信息并嵌入区域中的最不重要像素块.提取水印时,先按嵌入时一样将图像分区域,区域进一步分块,然后确定其中的LSPB 块,取LSPB 块的中心像素值即得到提取的水印w 1(i );又按嵌入水印时的相同算法由混沌映射计算出校验水印w 2(i ),则通过对比w 2(i )与w 1(i )就可知含水印图像是否发生篡改.对发生篡改的区域都进行标记(如像素点都置黑),最后得到的带标记文档图像即可定位所有被篡改的位置.此方法对文档图像的篡改和定位的检测灵敏度高,嵌入水印的文档图像可视质量好,水印提取不需要原始水印.文[22]将一个密钥采用Logistic 映射法生成混沌序列,并将其进行归一化后作为密钥流对原始水印数据进行置乱,产生经过加密的水印信息;然后采用Low [23-25]等人文章中的基于结构微调法,通过垂直调整文档的行间距、水平调整字符间距等将水印信息嵌入文档载体中,在提取时按照上述的嵌入规则来恢复水印信号,再使用正确的密钥得到原始的水印.由于混沌序列对初值的敏感性高,所以算法的抗攻击性强,并能够有效验证文档信息的正确性和完整性.由于文档图像格式各有不同,这种方法不能适用于所有二值图像.3.4　基于可编辑环境下的w ord 文档水印算法文献[26]根据人眼对于文字字体颜色的轻微变化不敏感的视觉特征(H VS )来进行文档水印的嵌入.它为w ord 文档中每组文字取6个比特的特征,将其分解为RG B 的3个分量;然后根据置乱密码的指示,找到指定文字,将其颜色值在原有的基础上加上字符特征码的分解值.由于每个分量只有两个比特,所以每个文字38第1期郗艳华,等:脆弱性数字文档水印技术48江西师范大学学报(自然科学版)2008年颜色变化的最大值为二进制序列(11,11,11),这样的变化,人眼是很难发觉的.所以算法通过提取文档的文字特征,利用用户密钥和哈希技术对其进行加密,得到含有密钥的水印信息并将其嵌入到待传输的文档中;接收端通过对含水印文档提取文档特征,并对其利用密钥加密得到水印信息,同时从含水印的文档中提取水印信息并将两者进行对比,即可判断文档的完整性,并可以定位文档篡改发生的位置.该方案不需要额外传送文档特征值.4　脆弱性文档水印技术有待进一步研究的方向现有的大多数文档水印算法主要集中在鲁棒性研究方面.但在实际应用中,大量的重要资料,如个人档案、医疗记录、专利证件、设计图样、支票、合同、证明或其他有价值的文档等几乎都是通过扫描转化为数字文档图像来进行保存和查阅的;电子信函、公文或传真等也几乎都以数字文档的形式通过网络传送,而完整性是对这些数字文档图像的最基本的要求.所以,如何对这些重要文档进行保护和完整性检测是一个很现实而又紧迫的问题.目前,脆弱性文档数字水印应在如下几方面进行深入研究:(1)基本理论的研究文档数字水印应该和其他脆弱性水印一样,其基础研究应主要包括建立合适的理论模型,分析文档水印信息的嵌入容量,建立基于人类视觉特性的水印系统模型,分析水印算法的安全可靠性等.(2)脆弱性文档水印算法的研究如何应用密码学、混沌理论等提高水印算法的脆弱性和安全性;如何在水印的嵌入容量和不可见性之间选择一个合适的平衡点;如何能够在对篡改进行定位的同时有效地恢复被篡改的部分;如何在变换域或以其它的方式(如基于自然语言等)开发出脆弱性文档水印算法,以及如何实现网络条件下电子文档水印的自动嵌入,开发自动嵌入系统,保证信息不被越权接收和发布使用,保证收发双方彼此信任等都是值得研究的问题.(3)脆弱性文档水印攻击算法研究水印设计和水印攻击通常是相辅相成,优秀的水印攻击算法有助于建立起更为完善的水印性能评价基准和技术标准,能促使人们研制出更安全可靠的数字水印系统.由于文档水印只处于刚刚起步阶段,水印攻击技术比较滞后,尤其是对脆弱性文档水印的攻击方法更少,所以该领域还有待于进行更进一步的探讨和研究.(4)脆弱性文档水印标准研究数字水印要得到广泛应用,应建立与其相应的标准,如水印嵌入标准、提取或检测标准和系统功能测试标准等.目前形成统一的客观评价各种水印算法的性能标准已成为研究者的共同目标,但标准的形成需要综合考虑各个方面的因素,才能够确保标准的通用性和有效性.在这方面如果有政府部门和各大公司合作,将会促进标准的形成,同时也有可能加速数字水印在实际中的应用.(5)脆弱性文档水印协议的研究一个完善的水印系统不只是需要几个好的算法,更需要安全有效的协议来保证.如果没有协议,攻击者就可以不受限制地访问水印系统,这时安全的水印方案也受到威胁.所以有关水印系统协议的研究也将是一个值得研究的课题.由于文本文档中的冗余空间少,因此可以嵌入的信息量小.与其它多媒体数字水印相比,文档水印的发展尚处于起始阶段,在理论和技术方面还需进行深入分析和探讨.随着电子文档的广泛应用,数字文档水印技术的研究定会越来越多,并将有大的突破.参考文献:[1]华先胜.易损数字水印若干问题的研究[J].中国图像图形学报,2001(11):108921095.[2]张小华.一类有效的脆弱型数字水印技术[J].电子学报,2004(1):1142117.[3]侯振华.脆弱性数字水印研究[J].计算机应用,2003(12):1062108.[4]宋玉杰.基于脆弱性数字水印的图象完整性验证研究[J ].中国图像图形学报,2003(1):127.[5]陈明奇.数字水印的研究进展和应用[J ].通信学报,2001(5):71279.[6]张宇.自然语言文本水印[J ].中文信息学报,2005,19(1):56262.[7]程玉柱.一种新的基于混沌映射的文本零水印算法[J ].计算机应用,2005(12):275422758.[8]M in Wu ,T ang E ,Liu B.Proc IEEE Int con f on multimedia and expositions[C].New Y ork :2000:3932396.[9]周波.一种适用于二值图像的数字水印算法[J ].上海交通大学学报,2004(9):150921514.[10]周永磊.结合数字水印和签名的二值图像内容验证[J ].计算机工程与应用,2004(15):55261.[11]Huijuan Y ang ,Alex C ,K ot ,et al.Semi 2fragile watermarking for text document images authentication[J ].IEEE International Sym posium on Circuits and Systems ,2005(4):400224006.[12]T zeng C H ,Tsai W H.A new approach to authentication of binary images for multimedia communication with distortion reduction and se 2curity enhancement[J ].IEEE C ommunication Letters ,2003,7(9):443.[13]张静.二值图像数字水印技术综述[J ].计算机工程,2005(2):123.[14]Wu M Y,Lee J H.Proc int sym p multimedia in form processing[C].T aiwan ,1998:3412346.[15]Tseng Y C ,Chen Y Y,Pan H K.A secure data hiding scheme for binary images[J ].IEEE T ransactions on C ommunications ,2002,50(8):122721231.[16]Tseng Y C.Data Hiding in 22color Images[E Q/O L ].[2007204218].http ://w w .tw /yctseng.[17]Chun 2e Zhang ,Zheng 2Ding Qiu.IEEE Proceedings of the F ourth International C on ference on Machine Learning and Cybernetics [C].2005:495224956.[18]王欣.一种针对汉字文本图像的数字水印技术[J ].计算机工程与应用,2005(12):57260.[19]张小华.一种基于外边缘的文档水印技术[J ].系统工程与电子技术,2003(5):6122616.[20]张小华.一种有效的文档水印技术[J ].通信学报,2003,24(5):22227.[21]朱从旭.一种灵敏的文本图像认证混沌脆弱水印技术[J ].小型微型计算机,2006(1):1512154.[22]徐蕾.利用数字水印技术验证文本信息的篡改[J ].微计算机信息,2005,21(3):59265.[23]J T Brassil ,S Low ,Maxemchuk N F.C opyright protection for the electronic distribution of text documents[J ].Proceedings of the IEEE ,1999,87(7):118121196.[24]Low S H ,F Maxemchuk N ,Lapone A M.D ocument identification for copyright protection for copyright protection using centroid detection[J ].IEEE T ransactions on C ommunications ,1998,46(5):3722383.[25]Brassil J ,S Low ,Maxemchuk N F ,et al.E lectronic marking and identification techniques to discourage document copying[J ].IEEE Jour 2nal on Sel Areas in C ommun ,1995,13(8):149521504.[26]唐承亮.基于文字RG B 颜色变化的脆弱型文本数字水印技术[J ].计算机工程与应用,2005,36:628.[27]张静.用于图像认证的数字水印技术[J ].中国图像图形学报,2003,8(4):3672373.On Fragile Document W atermarking TechniqueXI Y an 2hua ,　ZH ANG Min 2rui(1.School of C ommunications and In formation Engineering ,X i ’an University of Science &T echnology ,X i ’an 710054,China ;2.Department of Physics ,X ianyang N ormal C ollege ,X ianyang Shanxi 712000,China )Abstract :With the development of the netw ork and information technologies ,many paper documents can be saved and trans formed in digital form.S o how to protect the truth and the integrity of the digital documents is in dire need.The de 2velopment of the fragile text watermarking technique provides an im portant method to res olve this issue.In this paper ,the concept of the fragile document watermarking is introduced and the current research states are studied ,with em phasis on the existing alg orithms and the com paris ons of their advantages and disadvantages.Finally the authors point out the future research directions for fragile document watermarking techniques.K ey w ords :document watermarking ;fragile watermarking ;documents security(责任编辑:冉小晓)58第1期郗艳华,等:脆弱性数字文档水印技术。

数字图像中脆弱水印关键算法研究的开题报告一、研究背景和意义随着数字图像的广泛应用，保护数字图像的知识产权和完整性变得越来越重要。

一种常见的保护方法是使用数字水印技术，在数字图像中嵌入一些不可见的信息，以标识和保护原始图像。

但是传统数字水印技术存在着一些问题，如攻击者可以通过修改、裁剪、添加噪声等方法破坏水印，使得水印失效。

因此，研究数字图像中脆弱水印关键算法，对提高数字图像保护的能力具有重要意义。

与传统数字水印技术不同，脆弱水印是一种特殊的水印，容易被攻击者破坏。

但是，其在防抵赖和图像完整性方面具有独特的优势。

因此，脆弱水印技术在图像认证和防篡改方面具有广泛的应用前景。

二、研究目标和内容本文旨在研究数字图像中脆弱水印关键算法，探讨其在数字图像保护和认证领域的应用。

具体研究内容包括以下方面：1.脆弱水印的定义和分类：介绍脆弱水印的定义、分类和特点，对脆弱水印技术的基本概念进行详细阐述。

2.脆弱水印关键算法的研究：研究数字图像中脆弱水印的嵌入和提取算法，包括基于离散小波变换、主成分分析和奇异值分解等多种算法。

3.脆弱水印抵抗攻击性能的研究：探讨脆弱水印算法的抗攻击性能及其提高方法，比较各种算法在不同攻击条件下的鲁棒性。

4.脆弱水印的应用：研究脆弱水印技术在数字图像保护、认证和版权保护方面的应用，对其实际应用进行案例分析和评估。

三、研究方法本文将采用文献综述和实验研究相结合的方法进行研究，主要研究方法包括如下几个方面：1.文献综述：结合现有文献，对脆弱水印技术的基本概念、发展历程、分类和研究现状进行系统性梳理和总结。

2.算法设计：基于文献综述，设计脆弱水印嵌入和提取算法，并通过实验验证其有效性和性能。

3.攻击性能测试：对各种算法在不同攻击条件下的鲁棒性进行测试和分析，探讨提高脆弱水印抗攻击性能的方法。

4.应用案例分析：以版权保护为主要应用场景，分析脆弱水印技术在实际应用中的优势和不足，并提出相应的改进方法。

Optik122 (2011) 311–316Contents lists available at ScienceDirectOptikj o u r n a l h o m e p a g e:w w w.e l s e v i e r.d e/i j l eoBlind image watermarking based on discrete fractional random transform and subsamplingHao Luo,Fa-Xin Yu∗,Zheng-Liang Huang,Zhe-Ming LuSchool of Aeronautics and Astronautics,Zhejiang University,Hangzhou310029,PR Chinaa r t i c l e i n f oArticle history:Received11June2009Accepted27December2009Keywords:Blind image watermarkingDiscrete fractional random transform Image subsamplingTamper detectionSelf-embedding a b s t r a c tThis paper proposes a blind watermarking scheme based on discrete fractional random transform.The watermark information can be a binary sequence,a gray level image or a set of decimal fractions sampled from a given source signal.The host image is subsampled into four subimages,and the high correlations among their discrete fractional random transform coefficients are exploited for watermark embedding. Based on this self-reference strategy,the watermark can be extracted without the aid of the host image. As a fragile watermarking technique,our scheme can be used in tamper detection.Besides,it can be used in self-embedding for a large payload is provided.Meanwhile,security of the watermark is preserved due to the randomness of the discrete fractional random transform.Experimental results demonstrate the effectiveness of our scheme.© 2010 Elsevier GmbH. All rights reserved.1.IntroductionDigital watermarking[1]plays an important role in multimedia information security.It is a process of embedding some secret data in the host media such as images,videos,audios,3D meshes,etc.So far various digital image watermarking techniques have been pre-sented in literatures.These schemes can be categorized according to different aspects as follows.According to the watermark perceptibility,they can be divided into visible and invisible watermarking methods.Visible water-marking[2,3]can be used in applications such as copyright announcement and advertisement,while invisible watermark-ing[4–15]is used for covert communication,traitor tracing, etc.According to the watermark robustness against attacks,they can be divided into robust,semi-fragile and fragile approaches. Robust watermarking[4,5,8,9,16],usually designed for copyright protection,can resist most intentional or unintentional attacks,e.g., rotation,scaling and translation.In contrast,fragile watermarks [11]are sensitive to any alterations on the watermarked image including common image operations(e.g.,JPEG compression,low-passfiltering)and malicious attacks(e.g.,cropping).They can be used for content authentication,tamper detection and localiza-∗Corresponding author at:MinZhuGuan,HuaJiaChi Campus,Zhejiang University,No.268KaiXuan Road,Hangzhou310029,PR China.Tel.:+8657186971612;fax:+8657186971612.E-mail addresses:luohao@(H.Luo),aeeizju@(F.-X.Yu), hyland@(Z.-L.Huang),zheminglu@(Z.-M.Lu).tion,etc.Semi-fragile watermarking[15]can resist common image operations,while not robust to malicious attacks.According to the watermark embedding domain,they can be divided into spatial domain,transform domain and compres-sion domain based techniques.Generally speaking,the spatial domain based schemes[7,10,11]have lower computational com-plexity than the other two kinds of methods.For instance,the watermarking methods based on the least significant bitplane (LSB)modification[11,17]are such a kind of classical spatial domain based techniques.Watermarks in the transform domain based schemes[4–6,16]usually exhibit good robustness.The com-pression domain based techniques insert the watermark during compression or on compressed images.The associated com-pression techniques consist of JPEG[18],JPEG2000[19],vector quantization(VQ)[12–14],block truncation coding(BTC)[20],etc.According to whether the host image is required or not in watermark extraction,the available methods can be divided into blind,semi-blind and non-blind techniques.In the blind ones [6,7,9–11,21],the host image is not required.On the contrary,it must be provided in the non-blind methods[4,5].In the semi-blind schemes[16],the host image is not required while some prior knowledge is usually supplied as auxiliary information for watermark extraction.According to whether the host image can be perfectly recov-ered or not after watermark extracted,they can be divided into reversible and irreversible schemes.The host image can be per-fectly recovered in the reversible methods[10],while cannot in those irreversible[4–7,9,11–16].Generally a watermarking scheme may have two or more prop-erties as mentioned above.For instance,the methods in[4,5]are0030-4026/$–see front matter© 2010 Elsevier GmbH. All rights reserved. doi:10.1016/j.ijleo.2009.12.018312H.Luo et al./Optik122 (2011) 311–316not only robust but also transform domain based,while that in[22] is a reversible method in the VQ-compressed domain.Nowadays,most existing transform domain watermarking schemes are based on discrete cosine transform(DCT)[6,23],dis-crete wavelet transform(DWT)[8],discrete Fourier transform (DFT)[16],etc.Recently,Guo et al.[4]propose a novel transform domain watermarking method based on discrete fractional random transform(DFRNT).The DFRNT[24]is derived from the discrete fractional Fourier transform(DFrFT).In Guo et al.’s scheme,the phase shift keying(PSK)is adopted to adjust the phase of complex coefficients in the DFRNT domain with reference to the watermark bit to be embedded.This scheme is robust against some familiar attacks including cropping,noising and low-passfiltering.In2008, another DFRNT based watermarking scheme is proposed in[5]. It further demonstrates the method maintains higher robustness than those based on DCT,DFT and DFrFT.These DFRNT based watermarking methods have two common characteristics,i.e.,both of them belong to robust and non-blind techniques.However,in many scenarios,the host image cannot be released after watermark embedded.In those cases,the non-blind DFRNT based methods are no longer appropriate.In addition, as aforementioned,fragile watermarking can be widely used in tamper detection and self-embedding[7].Motivated by this,we propose a blind and fragile watermarking scheme based on DFRNT. In it,the watermark can be accurately extracted if the water-marked image is intact,without the host image aided.Moreover, its utilization in tamper detection,localization and recovery is also demonstrated.Our method starts from subsampling the host image into four equal-sized subimages.Then each subimage is transformed into DFRNT domain coefficients.It is easy to understand that high corre-lation among the spatial subimages’pixels still exists more or less in their corresponding DFRNT coefficients.Thus,watermark can be embedded and extracted with respect to the correlation of two subimages’coefficients.A high visual quality watermarked image can be acquired even if much watermark data is hidden.As long as the watermarked image suffers alterations,the coefficient cor-relation is destroyed.Therefore,our scheme can be used in tamper detection and localization.In particular,it can be easily extended for self-embedding.That is,if the host image content is used as a watermark,the tampered area can be recovered to some extent.The rest part of this paper is organized as follows.Section2 briefly reviews the DFRNT transform and two non-blind water-marking methods based on it.Section3extensively describes the watermark embedding and extraction procedures of our scheme. Experimental results are shown in Sections4and5concludes the paper.2.Related work2.1.DFRNT transformIn[24],Liu et al.introduced the transform named DFRNT for thefirst time.It can be used for one or two-dimensional discrete signal analysis.Specially,it is effective for image encryption[24], secret sharing[25]and watermarking[4,5].The DFRNT of a two-dimensional image I can be represented asC=R˛I(R˛)T(1) where R˛and˛denote the kernel transform matrix and the frac-tional order of DFRNT,respectively.The superscript T means matrix transposition.The output matrix C is the transform coefficients in DFRNT domain.To construct a transform matrix R˛,we must generate a random matrix P in advance asP=Q+Q T2(2) where Q is a real nonsingular matrix with elements randomly generated.Suppose(V=v1,v2,...,v n)denotes the normalized eigenvector matrix of P.Obviously,any two eigenvectors(two columns of V)are orthonormal because P is a real symmetric matrix. Then we can obtain R˛asR˛=VD˛V T(3) where D˛is a diagonal matrix defined asD˛=diag1,exp−i2 ˛t,...,exp−i2(N−1) ˛t(4)where t denotes the periodicity of DFRNT.Clearly,DFRNT is a random transform due to the randomness of R˛.In other words,different P or Q corresponds to different R˛,and further different DFRNT.Specifically,when˛=rt/2(r is a constant integer),the output of DFRNT is real for a real signal[24].2.2.Non-blind watermarking based on DFRNTThe two watermarking schemes based on DFRNT are briefly reviewed.In[4],the host image isfirstly transformed into DFRNT coefficients.Then this coefficient matrix is partitioned into a set of non-overlapping blocks.Next,in each block several coefficients with the largest amplitudes are selected for watermark embedding. The watermark is hidden by PSK,i.e.,slightly changing the phase of selected coefficient according to the watermark bit.At last,the watermarked coefficients are transformed into the watermarked image via inverse DFRNT.The watermark extraction is the inverse process of watermark embedding by comparing the DFRNT coef-ficients’phases of the host image with those of the watermarked version.Different from thefirst one,the second scheme[5]exploits the fact that when˛is set as half periodicity,the output of DFRNT remains real for a real signal[24].It starts from transforming the host image and the watermark image into coefficients in DFRNT domain,respectively.Then similar as the scheme in[4],some coef-ficients of the host image with the largest amplitudes are selected. The watermark embedding is achieved by slightly changing the amplitudes of these selected coefficients according to the water-mark image’s coefficients.At last,the watermarked coefficients are inverse DFRNT transformed into the watermarked image.The watermark extraction is also a comparison mechanism similar as that in thefirst scheme[4],with the host image needed.In these two DFRNT based schemes,the selected coefficients of the host image for watermark embedding are with largest ampli-tudes.This is because small variations on larger coefficients are more likely to result in smaller distortions of the host image intro-duced by watermark embedding.In this way,the imperceptibility of the watermark is better preserved.In addition,as the host image must be provided during watermark extraction,they belong to non-blind watermarking techniques.3.Proposed schemeThe main difference between our scheme and the reported two methods lies in the host image is not required in watermark extraction.That is,some substitute reference information must be provided for watermark extraction.Actually,much information redundancy exists in natural image pixels,especially in small local blocks.Thus,we can exploit this high correlation among them as reference information,namely the principle of self-reference.ThisH.Luo et al./Optik 122 (2011) 311–316313Fig.1.Block diagram of watermark embedding.self-reference strategy based on subsampling is similar to that in the blind watermarking method based on DCT [6].3.1.Watermark embeddingSuppose the host image I is a gray level image of size K 1×K 2,and the original watermark W is composed by a set of numbers with each element 0≤w ≤1.It can be a binary sequence,a gray level image or some decimal fractions sampled from a given source signal.All these secret data are normalized to the range of [0,1]before embedded.The block diagram of watermark embedding is shown in Fig.1with details described below.Firstly,I is subsampled into four subimages I 1,I 2,I 3,I 4of size (K 1/2)×(K 2/2)with the rule given as I 1(i,j )=I (2i −1,2j −1)I 2(i,j )=I (2i −1,2j )I 3(i,j )=I (2i,2j −1)I 4(i,j )=I (2i,2j )(5)where (i ,j )denotes the pixel’s coordinates in the subimages with i =1,2,...,K 1/2,j =1,2,...,K 2/2.This subsampling strategy is illus-trated in Fig.2where I is partitioned into 2×2non-overlapping blocks.To each block,the upper-left pixel (indicated by the trian-gle),the upper-right pixel (indicated by the circle),the bottom-left pixel (indicated by the square)and the bottom-right pixel (indi-cated by the diamond)are assigned to I 1,I 2,I 3,and I 4,respectively.Secondly,each subimage is partitioned into k 1×k 2non-overlapping blocks represented by B 1(k ),B 2(k ),B 3(k ),B 4(k )with k =1,2,...,L .Here L is the number of blocks of each subimage,and B 1(k ),B 2(k ),B 3(k ),B 4(k )denote the k th block of I 1,I 2,I 3,I 4,respectively.Thirdly,the DFRNT is performed on B 1(k ),B 2(k ),B 3(k ),B 4(k )indi-vidually,and the corresponding DFRNT coefficient matrices C 1(k ),C 2(k ),C 3(k ),C 4(k )are obtained.In this paper,the periodicity t and the fractional order ˛are set as 1and 0.5respectively.Hence,all the elements of C 1(k ),C 2(k ),C 3(k )and C 4(k )arereal.Fig.2.Image subsamplingstrategy.Fig.3.Selection of watermark embedding positions.Fourthly,C 1(k ),C 2(k ),C 3(k )and C 4(k )are classified into two groups with each group possess two coefficient matrices.Obviouslythere are totally P 24=12combinations.The grouping mecha-nism can be fixed or randomly determined by a pseudo-random sequence.In our case,we adopt a fixed order combination,i.e.,{C 1(k ),C 2(k )}and {C 3(k ),C 4(k )}.This means C 1(k )and C 2(k )belong to one group,and the other group is composed of C 3(k )and C 4(k ).For description simplicity,we only select the group {C 1(k ),C 2(k )}to explain the watermark embedding process in detail.The operations on {C 3(k ),C 4(k )}are exactly the same.Now our task is reduced to select some appropriate pairs of coefficients from the possible k 1×k 2coefficient pairs in the same positions of {C 1(k ),C 2(k )}.For the current coefficient pair (c a ,c b ),compute its mean value as m =c a +c b2(6)where a and b are integers in the range of [1,k 1×k 2].c a and c b are the DFRNT coefficients located at the same positions of C 1(k )and C 2(k ),respectively.In our case,the coefficient pair with a large mean value m is selected for watermark embedding.Thereby a relative small distortion is introduced to the host image and meanwhile the imperceptibility of the watermark is well preserved.If l watermark data are to be embedded in {C 1(k ),C 2(k )},the l pairs with the l largest mean values are selected.The following rule is applied to evaluate if the current pair (c a ,c b )is selected or not for watermark embedding:(c a ,c b )=selected m belongs to the l highest meansnon-selectedotherwise(7)The selection of watermark embedding positions is illustrated in Fig.3.Actually,this process is to compute the mean matrix M of C 1(k )and C 2(k ).Then several largest mean values in M are selected for watermark embedding.For instance,if l =4,the four largest mean values indicated by black points are chosen.If (c a ,c b )is selected,the current watermark w (0≤w ≤1)is embedded as Eq.(8),otherwise it is skipped and the next pair of coefficients are ually the watermark is permuted before embedding.c a=m (1+ˇw )c b=m (1−ˇw )(8)where ˇdenotes the watermark embedding strength.A larger ˇindicates higher watermark energy and accordingly more visual distortion is introduced.Repeat this operation for all pairs of selected coefficients,and thus four watermarked coefficient matri-ces C 1,C 2are obtained.Similarly,C 3and C 2are also obtained.At last,the inverse DFRNT is performed on C 1,C 2,C 3,and C 4,respectively,and the corresponding I 1,I 2,I 3,and I 4are obtained.These watermarked subimages are recomposed and the water-marked image I is obtained.314H.Luo et al./Optik122 (2011) 311–316Fig.4.Block diagram of watermark extraction.3.2.Watermark extractionAs shown in Fig.4,the watermark extraction is the inverse pro-cess of watermark embedding with the procedures described as follows.Firstly,the watermarked image I’is subsampled into four equal-sized subimages I 1,I 2,I 3,I 4,and each of them is partitioned into k 1×k 2non-overlapping blocks B 1(k ),B 2(k ),B 3(k ),B 4(k ).Secondly,the DFRNT is performed on B 1(k ),B 2(k ),B 3(k ),B 4(k )respectively,and the coefficient matrices C 1(k ),C 2(k ),C 3(k ),and C 4(k )are obtained.Then these coefficient matrices are divided into two groups {C 1(k ),C 2(k ),C 3(k ),C 4(k )}.Thirdly,the mean value of the current coefficient pair (c a ,c b)in the group {C 1(k ),C 2(k )}is computed as m=c a+c b2(9)Obviously m is equal to m no matter whether the watermark data is embedded or not.In other words,the mean value of each pair is not changed.Hence the coefficient pair whose mean value m belongs to the l largest mean values is found again.For instance,when l =4,the four positions indicated by black points as shown in Fig.3are accurately retrieved during watermark extraction.At last,the watermark bit can be extracted as w =c a−c bˇ(c a+c b )(10)This operation is repeated on all pairs of coefficients and until the entire watermark W is extracted after inverse permutation.4.Experimental resultsIn all the experiments,the DFRNT kernel transform matrix R ˛is generated by a uniformly random matrix Q ,and the watermark embedding strength ˇis set as 0.01.Each subimage’s coefficient matrix is partitioned into 8×8blocks.The metric peak signal to noise ratio (PSNR)is used to evaluate the visual quality of the water-marked image.For an 8-bit gray level image,the PSNR is defined asPSNR =10log 1025521/K 1×K 2K 1 i =1K 2 j =1(I (i,j )−I (i,j ))2(11)where I (i ,j )and I (i ,j )denote the gray levels of the pixel in the posi-tion of (i ,j )of the K 1×K 2host image and the watermarked image,respectively.A higher PSNR indicates smaller distortions due to the watermark embedded.The first experiment is to embed a 128×128binary watermark image as shown in Fig.5(c)in the 512×512gray level Lena image as shown in Fig.5(a).The four 256×256subimages of Lena and their associated DFRNT coefficients are shown in Fig.5(f)–(i)and Fig.5(j)–(m),respectively.As each coefficient matrix consists of 32×32blocks,there are totally 2048groups of blocks and 8water-mark data (in this case,0or 1)should be hidden in each group of blocks.Before embedded,the watermark image is permutedbyFig.5.Experimental results on embedding a binary image in Lena,(a)the host image,(b)the watermarked image with PSNR =90.53dB,(c)the original watermark,(d)the watermark extracted with the correct kernel matrix R ˛,(e)the watermark extracted with a wrong kernel matrix R ˛,(f)–(i)four subimages of Lena,and (j)–(m)the four DFRNT coefficient matrices corresponding to the four subimages.a pseudo-random sequence.The PSNR value of the watermarked Lena as shown in Fig.5(b)compared with the original version can be achieved as 90.53dB.Besides,if the watermarked image suf-fers no alterations,the watermark extracted with the correct kernel matrix R ˛as shown in Fig.5(d)is exactly the same as the original one.However,if a wrong kernel matrix R ˛(randomly generated)is used,the extracted watermark as shown in Fig.5(e)looks like random noise.This proves that security of the watermark content is guaranteed because of the randomness of DFRNT.Another five 512×512images Baboon,Barbara,Boat,F16and Peppers as shown in Fig.6are selected as host images toevalu-Fig.6.Five 512×512test images Baboon,Barbara,Boat,F16and Peppers (from left to right).H.Luo et al./Optik 122 (2011) 311–316315Table 1PSNR values with different watermark capacities embedded.ImagesCapacity 204840968192163843276865536Baboon 96.99dB 94.09dB 90.85dB 87.94dB 84.90dB 81.90dB Barbara 101.29dB 98.21dB 95.75dB 92.53dB 89.45dB 86.47dB Boat 102.56dB 99.35dB 96.46dB 93.58dB 90.77dB 87.86dB F16103.62dB 101.42dB 98.60dB 95.63dB 92.54dB 88.97dB Peppers 102.55dB 99.85dB 97.17dB 94.38dB 91.59dB 88.20dB Average101.40dB98.58dB95.77dB92.81dB89.85dB86.68dBate the average performance of our scheme.In this experiment,the watermarks are random number sequences produced by pseudo-number generators.They consist of 2048,4096,8192,16384,32768and 65536decimal fractions,respectively,with each element lies in the range of [0,1].Accordingly the numbers of watermark data carried by each group of blocks correspond to 1,2,4,8,16and 32,respectively.The PSNR values of the watermarked images are listed in Table 1.Generally speaking,human eyes cannot distinguish con-spicuous visual distortions of the watermarked image when its PSNR value is larger than 30dB.From Table 1,we can find that the computed PSNR values are much larger than this level.This implies the distortions introduced by watermark embedded are acceptable.To illustrate the tamper detection ability of our scheme,a 256×256binary watermark image as shown in Fig.7(d)is embed-ded in the 512×512host image Bridge as shown in Fig.7(a).The watermarked image is shown in Fig.7(b)with PSNR =84.51dB.Its corrupted version is shown in Fig.7(c)with the tampered positions indicated by two white circles.Clearly,the associated areas of the extracted watermark as shown in Fig.7(e)are also tampered.Note in this case it is unnecessary to permute the original watermark before embedding.In fact,our scheme can be easily extended for self-embedding.Self-embedding [7]refers to embedding part of the host image con-tent in itself for tamper detection,localization and recovery.The watermark in self-embedding is usually a highly compressed con-tent of the host image,e.g.,the low frequency DCT coefficients,the output of digital halftoning.This information can be used to reconstruct an approximate version of the host image.Generally,self-embedding watermarking schemes can provide a relative large payload such that a high fidelity version ofthe host image can be inserted and reconstructed.From Table 1,we find that a high PSNR value of the watermarked image can be preserved when a large quantity of secret data is embedded.Thus it is feasible to apply our scheme in self-embedding.As the example demonstrated in Fig.8,Fig.7.Experimental results on tamper detection,(a)the host image,(b)the water-marked image with PSNR =84.51dB,(c)the tampered watermarked image,(d)the original watermark,and (e)the extracted watermark.Fig.8.Experimental results on self-embedding,(a)the host image,(b)the water-marked image with PSNR =89.30dB,(c)the original watermark,(d)the tampered watermarked image,and (e)the extracted watermark.the 512×512host image is shown in Fig.8(a).The 256×256orig-inal watermark is shown in Fig.8(d)with its pixel values being the mean values of each 2×2block of Fig.8(a).These pixel val-ues are normalized into decimal fractions between 0and 1before embedded.The watermarked image is shown in Fig.8(b)with PSNR =89.30dB.The corrupted watermarked image is shown in Fig.8(c)with the tampered area indicated with a white circle,where one of the three original trucks in the watermarked image is copied and pasted.The extracted watermark is shown in Fig.8(e).From the extracted watermark,the tampered area is detected,localized and its content in the original image is recovered approximately.5.ConclusionsA blind image watermarking scheme based on DFRNT is pre-sented in this pared with the available DFRNT based methods,it exploits the high correlation among four subimages of the host image as self reference information for watermark extraction,and thus the host image is no longer required.This improvement expands the application scenarios in the cases that the host image cannot be released or obtained.As a fragile water-marking technique,our scheme can be used for tamper detection.Furthermore,if the content of the host image is employed as the watermark,the tampered area can be localized and recovered approximately.If a wrong DFRNT kernel matrix is used in water-mark extraction,no meaningful watermark information can be revealed.That is,security of our scheme is perfectly preserved by the randomness of DFRNT kernel matrix.AcknowledgmentsThe authors would like to express sincere thanks to Dr.Zhengjun Liu,Harbin Institute of Technology,PR China,for valuable discus-sions.316H.Luo et al./Optik122 (2011) 311–316References[1]J.S.Pan,H.C.Huang,L.C.Jain(Eds.),Intelligent Watermarking Techniques,World Scientific Publishing Company,Singapore,2004.[2]H.Luo,J.S.Pan,Z.M.Lu,Content adaptive visible watermarking during ordereddithering,IEICE rm.Syst.E90-D(2007)1113–1116.[3]J.S.Pan,H.Luo,Z.M.Lu,Visible watermarking for halftone images,IEICE Trans.put.Sci.E90-A(2007)1487–1490.[4]J.Guo,Z.Liu,S.Liu,Watermarking based on discrete fractional random trans-form,mun.272(2007)344–348.[5]Q.Guo,Z.Liu,S.Liu,Robustness analysis of image watermarking based ondiscrete fractional random 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一种能抵抗协议攻击的脆弱水印
胡云;郭芬红;杨义先
【期刊名称】《信号处理》
【年(卷),期】2003(019)0z1
【摘要】针对Wong提出的系列水印算法,本文给出了一种有效的协议攻击并提出了一种新的能够抵抗该协议攻击的脆弱水印算法.新算法将鲁棒性水印与脆弱性水印相结合来构造图像完整性和认证性水印.实验表明新算法不但能抵抗协议攻击,而且具有隐蔽性,能检测图像大小和像素值的改变,并能定位被改变的像素.此外,由于有了鲁棒性水印,增加了对版权的认证能力.
【总页数】4页(P153-156)
【作者】胡云;郭芬红;杨义先
【作者单位】北京邮电大学信息安全中心,北京,100876;北京邮电大学信息安全中心,北京,100876;北京邮电大学信息安全中心,北京,100876
【正文语种】中文
【中图分类】TN91
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