基于离散小波变换系数特征的2D被动盲图像取证研究
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摘要
数字媒体是现代数字时代最主要的通信工具之一。数字视频和图像已经成为最主要的信息载体。目前,主流媒体、法庭证物、时尚杂志、科学刊物、政治运动工具和互联网等越来越多地以数字图像的形式使用、存储和传输数字可视化数据。然而,随着近年来否认视觉图像真实性的技术发展,数字图像的可靠性遭受到质疑。
一方面,用户中心和极具吸引力的技术(如Web2.0)的发展导致数字图像的使用、存储和传输不断地增加。如Web2.0提供了用户友好和用户生成的服务,如博客、wiki和社交网络等。因此,每个普通的计算机用户可存储和分发数字图像变得更容易。
另一方面,强大的图像处理工具如Adobe Photoshop等的广泛应用使普通计算机用户篡改数字图像变得更简单、熟练和合理。目前,恶意伪造数字图像的方式多种多样,其中最常见的方式有二维图像区域复制粘贴、二维图像拼接和三维计算机图形渲染。下面我们将从图像篡改的起源开始,介绍上述最常见的篡改类型。
图像篡改或伪造是通过一系列操作来操纵图像内容的行为,严格来讲,这些操作包括对比度增强、去除红眼等非进攻性处理以及对图像区域进行复制或合成的恶意处理。因此，图像篡改暗含着将伪造图像负载的信息来获得无根据的图像。
图像篡改的历史与摄影技术一样的悠久。在过去,可以由专家通过使用彩色喷雾压力枪来进行喷绘,通过特殊染料的合适大小点画笔进行润饰,或通过特种滤光器进行对比度和颜色调整等来创建篡改图像。这是一个相对简单的任务,因为其涉及到的技术在当时并不复杂。
然而,越来越多的免费或易负担的强大图像处理和编辑软件,如Adobe Photoshop、 ACD See、iPhoto等的广泛使用,使图像处理相对比较容易,甚至一个缺乏摄影技巧的人也可以轻易做到。如今,存在许多不可感知地修改数字图像的方式,且大多方式可以轻易被一个计算机的普通用户所掌握。下面我们仅仅以下述最常见的方式为例来说明图像篡改。
常见的图像攻击之一是区域复制：也称之为区域克隆或复制-粘贴图像篡改(CMIF)。同幅图的区域复制篡改,即复制并粘贴图像中的一个部分到同一幅图像中的不同位置。通常,图像篡改操作是为了隐藏图像的一些细节信息,这种情况下将复制背景信息。当然,也可能为了增加更多的细
通常情况下,这样的图像篡改不容易发现,因为他们复制的内容来自同一幅图像,它也可以做的目的是增加更多详情。在这种情况下,使得至少有一个对象被克隆。复制粘贴攻击往往是潜移默化的,因为复制的片段粘贴从而使调色板,噪声元件,动态范围和其他性能与图像的其余部分兼容。此外,攻击者可以利用几何处理的复制片段,从而粘贴和融入他们的目标环境。
另一种常见的篡改类型是二维图像拼接。这也被称为图像合成。在图像拼接伪造中,复制图像的一部分,然后粘贴在相同的图像,或不同图像的不同位置上。涉及至少两个图像的图像拼接伪造。在人眼来看,拼接攻击是不明显的。
为了产生由两个或两个以上的图像拼接区域无缝合成图像,羽化操作经常被使用。羽化操作是悄悄地融入其周围粘贴区域的拼接图像的数学运算。基于羽化操作的拼接图像的明显结果是在色彩空间中的图像是去除伪造的拼接痕迹。然而,在图像中总是有通过拼接产生的不连续界线。一个很好的算法是利用图像篡改来证明不连续性。
第三个最常见的图像篡改是三维计算机图形渲染。在三维计算机图形渲染篡改中,逼真的图像合成具有逼真的颜色和纹理的计算机图形。逼真的图像,给出了多个视图和灯光照片的汇总外观。
因此,自动评估数字图像真实性的图像被动盲取证(PBIF)方法不能不引起广泛的重视。PBIF方法是在没有预先嵌入任何信息如水印或签名的情况下评估数字图像的真实性。针对以下几种图像篡改情况,本文提出了四种新颖的、简单有效的PBIF方法：
(1)由于高信噪比(SNR)的加性噪声或高压缩因子的有损压缩受到较小影响的二维图像区域复制粘贴。
CMIF检测算法最初的任务是确定对于一个给定的图像,无需任何先验知识的复制区域形状和位置是否包含克隆地区。一个最显然的方法来完成这样的任务是详尽的比较每个可能的地区。然而,这样的做法是复杂以成倍的增加。
Fridrich率先提出相对高效的块匹配方法。他们的开创性工作是中离散余弦变换计算作为一个特征代表各个重叠块。类似JPEG的量化离散余弦变换系数,按字典顺序排序比较有效的相似性。尽管如此,该技术存在鲁棒性不够和复杂度较高的缺点。
利用类似的方法,Popescu提出了在尺寸上通过PCA表示每个重叠区块来减少特征向量。该技术大大降低了计算的复杂度。它也被证明是强大的轻微像素变化对于加性噪声和有损压缩。由于大多数这些方法是在空间域中操作,它们是计算复杂度还是较高。
针对这类篡改,本文提出了一种PBIF解决方案：首先对整幅可疑图像进行离散小波变换(DWT),提取图像的低频子带^DWT是必须的，以对图像进行降维。
DWT将任何给定的图像分解成四个子带。低频子带包含了图像的大部分能量。因此，近似的图像尽可能和图像特征和其他结构有关。第二个子带给出了更多的细节信息，这些与水平方向上像素之间的差异有关。因此，当需要图像的水平方向梯度信息时，该子带图像是非常有用的。第三个子带提供了更多的细节信息，这与在垂直方向的像素之间的差异有关。因此，类似于第二个字带，该子带图像在需要垂直梯度信息时非常有用。第四个子带提供了与对角线方向像素之间的差异有关的细节信息。因此，与第二、第三子带类似,此子带在需要对角线图像梯度信息时非常有用。只有通过提取低频子带来维持大部分的图像信息，同时极大地减少了图像的空间。这就是为什么本文提出的算法在最初计算可以图像的DWT来减少图像的维度。一个固定大小的窗口按像素逐步在子带上滑动，提取特征向量在每个像素位置。
主成分分析-特征值分解（PCA-EVD),对所提取的特征进行之前，他们与字典排序的效率比较相似。
主成分分析（PCA),是众所周知的多元分析技术。主成分分析法的核心思想是降低有大量相互联的变量的数据集的维数，同时尽可能保留原始数据变化情况。PCA的涉及描述了一系列互不相关的变量方差的线性组合。第一主成分是单位长度具有极大的总体方差的变量的线性组合。第二主成分是线性组合的所有单位长度的线性组合不相关的第一主成分之间的最大差异。未来最大方差不相关变量的线性组合，如继续扫描，直到尽可能保留原始数据变化。
对于一个半正定对称矩阵，发现主要成分是计算求解特征值特征向量的问题相同。PCA-EVD影碟不仅降低了特征向量的维数，而且还消除小波系数的微小变化。
位移向量方法是用来筛选出与可能是复制区域无关的匹配块。移位向量是一个有序对,反映了从原始图像中的对象或区域到复制和粘贴对象或区域间位置坐标的差异。这里的主要思想是，一个可能的重复区域可能包括复制是相互关联的，而不是几个小型的，孤立块小块。因此地方重复做，我们期望有相同的位移量转变的若干小块。因此，这些块将具有相同的位移向量。这是转移载体的核心目的。它也被称为移位向量过滤财产。它过滤了所有那些转移向量不会累积到一个数量大于预定阈值相似的块。
提出的PBIF法不仅简单，而且对SNR高于24dB的加性噪声和质量因子大于70的JPEG压缩的弱攻击是鲁棒的。据观察，重复区域的大小会影响检出率，尤其是在图像JPEG压缩以及加性高斯噪声的图像。如果重复区域的大小是那么大，一般的结果提供更好的检出率。同样，如果JPEG压縮的质量是高的，结果表明较高的检出率。同样，如果信噪比高，检出率也高。在篡改图像处理后，通过未修改的情况下，算法的检测率是100%，为各种规模大于扫描正方晶格的大小，复制的区域。此外，该方法的主要步骤用一幅简单图像作为简化示例进行了说明，增强了对算法的理解。
(2)由于低信噪比(SNR)的加性噪声或低压縮因子的有损压缩受到较大影响的二维图像区域复制粘贴
我们的重点仍然是二维区域克隆或CMIF。然而，我们打算加强CMIF检测，甚至在很大程度上受到额外噪音或有损压缩影响信号的图像。在文献中，各种强大的被动法医方法最近被设计到检测区域克隆的图像操作,只有那些使用块特性方法提取的特征向量的方式在他们最近的算法中介绍。
Luo提出了一个定义7元组特征向量代表各个重叠块的方法。每个特征向量的前三个组件组成整个块的平均值，合并在过去的四个组成部分是块在不同的划分方向，表示整个块的总和的一半金额比率。由于特征向量的组成部分是浮点数，使用字典排序的特征向量排序。该算法的缺点是，它在空间域使用悬而未决的整体图像运作。第二个弱点是字典排序方法。因此，该方法计算复杂。
类似的方法，Lin定义一个9维特征向量，其组件也基于像素强度统计。此外，通过底层操作，组件被四舍五入为整数.因此基数排序是用来排序的特征向量。然而，该算法适用于空间域的整体形象。所以，该算法仍然是复杂的。
主要受加性噪声或有损压缩影响的图像复制粘贴区域只能通过PBIF方法提取更鲁棒的特征来进行检测。本文提出的解决方案通过采用基于块特征（BC)的方法进行特征提取。首先，对整幅可疑图像进行DWT变换，并只提取低频子带；然后采用基于块特征的方法提取特征。
虽然一般DWT中的微小变化将被PCA-EVD截断所删除，在前面的算法中提取的载体功能，仍然受到像素变化影响。因此建议PBIF算法在篡改图像的JPEG压缩或加性高斯噪声影响的情况下降低重复区域的重复检出率。
针对这个缺点可能的解决方案是使用其组件为归一化系数块特征的DWT系数的特征向量，而不是个别的DWT系数。聚类成块的系数具有量化效果，从而使特征向量对像素变化不够敏感。
基于算法所提取的特征的BC，具有更强的诱导量化效果。因此，相对于那些其组件为单一系数或者由可疑的图像经过DWT变换得到的系数，它更具有鲁棒性。
采用基数排序法对提取的特征向量进行字典排序比较它们的相似度。当它对所提取的特征进行排序，基数排序采用整数键。因此，当它被提取的特征向量排序的排序，基数排序算法的计算复杂度远远小于字典，因为字典排序使用浮动键，并采用位移矢量过滤那些孤立的匹配块。
基于BC的方法不仅比基于PCA的方法速度快，而且对加性噪声和JPEG压缩更加鲁棒。图像复制区域在添加信噪比只有20dB的加性噪声或进行质量因子为40的JPEG压縮后也能被检测出来，且准确率高达95%。
据观察，重复区域的大小会影响检出率，尤其是在图像JPEG压缩以及加性高斯噪声的图像。如果重复区域很大，一般的结果提供更好的检测率。同样，如果JPEG压缩的质量很高，则检测率也很髙。同样，如果信噪比高，检测率也高。在后处理未修改的篡改图像的情况下，如果重复区域的尺寸比扫描正方晶格大，算法的检测率可达到100%。
由于用于算法中的基于特征向量的每个BC包含量化组件，所以算法的一方面是对加性噪声和有损压缩具有更强的鲁棒性。此外，也可能是因为特征向量是使用快速基数排序而不是词典排序，使得算法具有较低的复杂度。
然而，算法的另一面是，在检测重复区域时其抗旋转和缩放性，继承了移位向量的不足。己提出的一个特设的伪自动方法来处理这个问题。但它不能被视为一个完整的解决问题的方法，因为它不是全自动的。这个缺点可能的解决方法是使用验证的方法来代替移位向量方法，这种方法对重复区域的几何处理不够敏感。因此，这些能够克服移位向量的缺点的算法是很必要的。
(3)复制区域经过仿射变换操作（如翻转、旋转、縮放）的2D图像区域复制
图像的几何形变操作的需要，在2D图像处理中很常见。其目的是消除由相机引起畸变和复制攻击的痕迹。这种简单的几何操作存在于大多数现有的图像复制检测方法中，尤其是那些移矢量管行无法准确检测的复制。
如果特征提取和验证或者选择方法对于几何操作鲁棒，那么PBIF方法就能检测这种复制。因此，在这些特征向量的提取阶段和验证阶段的复制区域的几何操作是不敏感。
本文设计的具有几何鲁棒性的新PB1F方法提取了基于块特征的仿射不变特征。为有效比较特征向量的相似度，用基数排序法对特征向量进行字典排序。
选择方法部分源于以下的技术进步。Amerini曾一度提出恢复图像中的仿射变换参数或一个区域的几何操作的想法。在他们的研究中，作者利用单应性的最大似然估计在同质坐标仿射变换参数。
然而，Christlein提出一个简单、直接恢复图像在二维笛卡尔坐标的仿射变换参数的方法，叫做SAT (相同仿射变换选择)。SAT是一种常见的替代选择和验证，用于矢量移位的方法。像移位矢量，SAT拥有的离群过滤的特性。但是，SAT是不敏感的仿射变换，它恢复几何操作区域的仿射变换参数略有增加计算的时间成本。因此，SAT更适合用于图像区域经过平移，旋转或缩放的图像区域复制。
同时，采用相同的仿射变换选择（SATS)方法来过滤孤立的匹配块。与位移矢量不同，SATS对几何篡改攻击是不敏感的。本文提出的PBIF方法能有效并高效的检测出经过仿射变换的篡改区域。值得注意的是，结果表明，该算法的平均推荐精度的情况下重复区域仅仅是翻译或反射。在高检测率也登记的情况下重复区域受到JPEG压缩或附加噪声。重复区域的仿射变换的旋转或联合形式的影响是一个值得推荐的准确性。重复的区域受到缩放或者旋转任意角度，该算法的准确度将稍微降低。较低的检测结果是由于局部发生时通过缩放或旋转任意角度的重复侵袭的区域是由固定的窗口扫描的像素交流。然而，在一般情况下，我们注意到，该算法的准确性随着重复区域大小的增加而增加。
从结果可以看出，该算法优于现有的算法，尤其是在精度和计算复杂性方面。它也优于SATS的这些算法在计算复杂性方面的管道，因为不像大多数悬而未决可疑图像在空间域上运行这种算法，该算法在较低频率的小波子带运行。SATS的唯一算法，提出了由Christlein个人小波系数表现不佳（127检测可能重复2588)。相比之下，我们的算法在执行BC上的小波系数的SATS，具有更高的精度。
其他算法的优势，包括每个BC基于特征向量提取的算法，由于他们是由通过量化块平均和下截断操作组成的，更强大的加性噪声和有损压缩算法。此外，因为使用更快的基数排序，而不是字典排序进行排序的特征向量，使得该算法具有较低的复杂度。最后，由于特征向量不变仿射变换和仿射变换的弹性重复验证方法，选择相同的仿射变换使用，该算法更强大的仿射变换。
然而，该算法的弱点之一是其检出率下降情况下，旋转任意角度，而不仅仅是九十度的倍数。这是因为，大部分像素保持在这种旋转的同心广场，一些像素仍然丢失在过程中将会获得新的像素。局部的像素交换上的微小差异而引发的组件可能逃脱的量化效果和地面操作的平均。这个弱点可能的解决办法是设每个同心圆有一个多边形的侧面的数量大于4，只考虑那些像素子块内的多边形。我们认为，更大的边数将会达到更好的检测精度。在不久的将来，我们将探讨可能的解决方案。
另一个弱点是，该算法仅适用于特定类型的图像在相同的图像伪造复制的粘贴。因此，需要有以下算法来解决该问题。
(4)两幅或更多幅图像的2D图像拼接
数字图像拼接伪造是一个特定类型的图像篡改。在图像拼接伪造中，复制图像的一部分，然后粘贴在相同的图像，或在不同的图像的不同位置上。一个拼接图像可能会有或没有拼接后处理，如羽毛操作。在这两种情况下，由拼接过程中引入的工件可能人眼几乎无法察觉。
许多图像拼接检测方法已经被设计出来。显著的例子包括由Ng提出的在拼接过程中引入的操纵图像的相干高阶矩谱检测尖锐的连续性。然而，该方法的检出率并不令人鼓舞。
另一个突破性的图像拼接检测工作是由Johnson提出的利用不一致的照明检测图像造假技术。这个技术对于检测有着相似照明条件的拼接图像没有效果。此外，它是悬而未决的图像，因此复杂。
图像拼接检测的另一个企图是由陈中的2-D相位一致性和特征函数的统计矩雇用卡车拼接故事。然而，特征提取的过程非常耗时。此外，检出率并不反映涉及的努力。
检测图像拼接的基础上，灰度共生矩阵是从图像中提取二阶纹理信息知名的代表性。在色度空间的边缘图像的灰度级共生矩阵进行了研究。应用在拼接文物通常不被伪装的可疑图像的色度空间的方法，提高了算法的有效性。然而，尽管执行的特征尺寸减少，方法通常是复杂的。例如，它使用巨大的特点和悬而未决的可疑图像在空间域上进行。
本文提出的PBIF方法首先提取可疑图像经DWT后的低频子带，然后用固定大小的重叠方块平铺在各个颜色通道的子带，并计算每一个像素位置的局部极大梯度（LMPG)，得到了LMPG图像。随后计算LMPG图像每一个像素点的局部复杂度。LMPG不仅反映了DWT系数变化的程度，还通过消除除像素值突变外的大部分图像信息来对图像进行去相关。同时，局部复杂度决定了系数变化的频率。两个过度区域的适当阈值估计了图像拼接中的孤立痕迹。该PBIF方法能简单有效的检测2D图像拼接篡改。
该算法具有较好的时间复杂度比现有的算法在空间领域的运作，最初.因为该算法减少4的权力因素可疑图像的尺寸约。其次，LMPG减少所造成的梯度计算复杂性，因为它是一个线性的数量。同样，本地的复杂性，降低熵的复杂性。因此双方LMPG和地方的复杂性要少得多，分别比本地产生的梯度（Sobel算子，LOG)和地方在现有的方法，通常使用的熵计算复杂的操作。
在追踪连续性拼接的方法，检测的基本假设推出时，两个或两个以上的图像区域缝合在一起，这种不连续性有尖锐的过渡。一个正常的过渡区域，如对象的边缘或边界将有相对大量的像素之间的过渡梯度分布。这意味着，这些地区将不会有一个单独的像素位置高梯度，但显然有较高的局部熵或当地复杂。伪装后处理操作，如羽毛操作拼接地区模仿正常的物体边缘或边界的素质。未经处理后拼接地区将有高梯度和更低的复杂度或熵的过渡，因为被分配到一个像素的数量有限。
根据基本的假设，很显然，LMPG和局部复杂度提供了足够的信息来区分这些不连续性，同时避免了度量产生的梯度和熵时的计算复杂度。在提出的算法中所描述的LMPG和局部复杂度的合适阈值隔离了拼接的痕迹，其有效性就如由阈值所分别生成的梯度和熵，但具有较高的时间效率。此外，如果选择色度空间，将提高检测精度，因为拼接的痕迹在这样的空间中是公开的。
我们必须强调的是我们建议该提出的算法在YCbCr色彩模型的色度空间中进行。它是建立在这样基础上，一般情况下大多数图像拼接和图像操作都是在RGB色彩空间中进行的。因此，有很大的可能性使得拼接攻击将在R，G和B这样的颜色通道中被很好地掩盖起来。而当图像操作受其他色彩模型如YCC或YCbCr等的影响时，注意力将主要放在了亮度通道中。这时极有可能使拼接痕迹在Y通道中不明显而在色度通道中非常明显。在色度空间中的这种一般的忽视将会错过揭示色度空间中的篡改信息。
最近的研究工作表明如果将单一颜色模型下的一幅不可感知拼接的图像转换到其他颜色模型下，那么该图像将揭露出篡改的信息。特别是，在RGB色彩空间中无明显拼接痕迹的图像，当从YCbCr色彩模型的色度空间进行检测时将显示出拼接的痕迹。因此，为了获得更好的检测结果，我们建议在色度空间执行该提出的算法。这并不一定意味着该算法无法检测到在RGB色彩空间或亮度-色度颜色空间的Y通道中进行的拼接操作。
该算法的主要优势在于局部复杂度的降低以及通过为LMPG选择花销更低的迁移权值降低了计算复杂度。另外，实际上，实现图像拼接检测相当于在一个强图像内容信号中检测弱信号存在性的任务。该算法聚焦在色度空间中是有利的，这是因为图像内容构成的强信号将因为拼接不连续而被删除来保持目标弱信号。因此，通过颜色模型之间的图像转换’拼接检测任务减轻了存在于另一个弱背景信号中弱信号检测的难度。伪自适应阈值胜过了这一优势。该算法的性能非常不错，其整体结果的精度约为82°/^
然而，该算法仍然面临着许多挑战。例如，伪自适应阈值，尽管他们优于大多数现有方法中采用的阈值，但仍尚未完全自适应。在不久旳将来，我们将进行广泛搜索来获得自适应的阈值。此外，在经受后拼接处理和正常的图像边缘处理后的拼接不连续性间的界线是非常敏锐的，以致本文提出的算法能很容易地阻止误报。“个可能的解决方案是在证实最终检测结果之前，在中间检测结果上融入一个合适的保真度值。在未来的工作我们将重点研究如何获得这个保真度值。
一般的PBIF设计方案偏向考虑检测率的有效性，在本文中提出的PBIF方法在设计中则偏向考虑检测率的有效性和复杂度的折中。前一种设计方案的主要目的是设计一个比现有方法检测率有所提高的PBIF方法，但极少关注设计的PBIF方法的复杂度。为提高检测率寻找可行的特征经常会导致高的计算成本。然而，本文的设计方案的主要的目标就是设计使高检测率和低计算复杂度协调一致的PBIF的方法。这个设计方案目标的转变是合理而必要的，因为现在更多计算能力有限的微型设备融合了捕获、读取和显示数字图像的能力。随着需求的增加，这些微型设备要求使用有效而简单的自动PBIF方法来评估图像的真实性。
In this digital era, digital visual media represent one of the principal means of communication. Digital videos and images have coherently become the main information carriers. Currently, the mainstream media, courtroom exhibits, fashion magazines, scientific journals, political campaign tools and the internet are all experiencing increasing usage, hosting and transmission of digital visual data in form of digital images. However, the reliability of digital images has lately been questioned mainly because the visual imagery is experiencing contradicting technological developments. On one hand, the development of user-centric and attractive technologies such as Web2.0, which provide user-friendly and user-generated facilities such as blogs, wikis, and social networks, has resulted in increased usage, hosting and transmission of digital images. It has become more affordable to an average computer user to store and distribute digital images.
On the other hand, the availability of powerful image processing tools such as Adobe Photoshop has enabled the average computer user to doctor digital images with increasing ease and sophistication. Currently, there are numerous ways of maliciously forging a digital image. Among the most common ways of digital image forgery are the2D region duplication,2D image splicing and3D computer graphic rendering. We start by briefly describing these common kinds of image attack as follows
Region Duplication:This is also known as a region cloning or copy-move image forgery, CMIF. In a region duplication forgery, a part of an image is copied and then pasted on a different location within the same image. Usually, such an image tampering is done with the aim of either hiding some image details, in which case a background is duplicated. It may also be done with the aim of adding more details in which case at least an object is cloned. Only one image is involved in the region duplication forgery.
2D Image Splicing:This is also known as image compositing. In an image splicing forgery, a part of an image is copied and then pasted on a different location within the same image or in a different image altogether. At least two images are involved in image splicing forgery.
3D Computer Graphic Rendering:In a3D computer graphic rendering forgery, a photorealistic image is synthesized from augmenting a computer graphic with color and texture. The photorealistic image is given an aggregated appearance of photographs of multiple views and lightings.
It is clear that the need for passive and blind image forensics, PBIF, methods to automatically assess the authenticity of the digital images cannot be overemphasized. PBIF methods assess the authenticity of digital images in absence of embedded schemes such as watermarks or signatures. In this dissertation, we propose four new, effective and non-complex PBIF methods as automated solutions to the following image tampering:
(1)2D region duplications in which the duplicated image regions have been affected by minor variations due to additive noise or lossy compression where both signal-to-noise ratio, SNR, and compression quality are high;
The primary task of a CMIF detection algorithm is to determine if a given image contains cloned regions without having any prior knowledge of the shape and location of the copied regions. An obvious approach to accomplishing such a task is to exhaustively compare every possible pair of regions. However, such an approach is exponentially complex.
The proposed PBIF solution firstly performs the discrete wavelet transform, DWT, of the whole suspicious image and extracts only the low frequency subband to approximate the image. DWT is necessary to reduce the dimension of the image. A fixed size window is then slid over the subband, pixel by pixel, extracting a feature vector at each pixel location.
Principal component analysis-eigenvector decomposition, PCA-EVD, is subsequently performed on the extracted features before they are lexicographically sorted for efficient comparison for similarities. Principal Component Analysis, PCA, is a well known technique for multivariate analysis. The core idea of PCA is to reduce the dimensionality of a data set which has a large number of interrelated variables, while retaining the original variations of the data as much as possible. In addition, PCA-EVD not only reduces the dimension of the feature vectors, but also removes minor variations in DWT coefficients.
Shift vector approach is used to filter out matching blocks which are not connected into possible duplicated regions. A shift vector is an ordered pair of the differences in the coordinates of the positions or locations of a copied and relocated object or region in an image from the original object or region in an image.
The proposed PBIF method is not only non-complex, but also robust to weak attacks of additive noise as along as SNR is above24dB and JPEG compression as long as the quality is above70.
Furthermore, the major steps of the proposed PBIF algorithm are illustrated through a simplified example involving a toy image, which enhances the explanation of the algorithm.
(2)2D region duplications in which the duplicated image regions have been affected by major variations due to additive noise or lossy compression where both SNR and compression quality are low;
Our focus is still on detecting2D region cloning or CMIF. However, we intend to enhance detection of CMIF even in images where the signal is heavily affected by additive noise or losy compression.
Duplicated regions which are affected by major variations due to additive noise or lossy compression can only be detected by PBIF methods which extract more robust features. In the proposed solution, a block characteristic, BC, based approach to feature extraction is taken. Initially, DWT of the whole suspicious image is performed and only the low frequency subband is extracted to approximate the image. BC based features are extracted.
The BC based features which are extracted in this algorithm have more induced quantization effect. Therefore, they are more robust to attacks than the feature vectors whose components are individual pixels or coefficients of DWT transform of the suspicious image.
Radix Sort is used to lexicographically order the feature vectors for efficient comparison for similarities. Radix sort uses integer keys when it is sorting the extracted features. As a result, the computational complexity of the Radix sort algorithm is much smaller than that of the lexicographical sort because lexicographic sort uses floating keys when it is sorting the extracted feature vectors. Shift vector filters out isolated matching blocks.
The BC based method is not only faster than the PCA based method, but also more robust to additive noise and JPEG compression. It is capable of detecting, with efficiency as high as95%, duplicated regions affected by either additive noise where the signal is as weak as20dB, or JPEG Compression where the quality is as low as40.
(3)2D region cloning in which the duplicated image regions have been affected by affine transformation operations such as reflection, rotation, or scaling;
It is not uncommon for an image attacker to reflect a duplicated region, rotate the region through an arbitrary angle or scale the region, in addition to translating it. A PBIF method can only detect such duplications if both the extracted features and the verification or selection method are robust to those geometric operations. The designed new geometrically robust PBIF method extracts BC based features that are invariant to affine transformation. Radix Sort is subsequently used to lexicographically order the feature vectors for efficient comparison for similarities.
At the verification stage, a same affine transformation selection, SATS, is used to filter out isolated matching blocks. Unlike shift vector, SATS is insensitive to geometric attacks. The proposed PBIF method effectively and efficiently detects regions affected by affine transformation
(4)2D digital image splicing, a kind of forgery in which two or more images are involved.
A digital image splicing forgery is a specific kind of image tampering. In an image splicing forgery, a part of an image is copied and then pasted on a different location within the same image or in a different image altogether. A spliced image may be with or without post-splicing processing, such as feather operation. In either case, the artifacts introduced by the splicing process may be almost imperceptible to the human eye.
The proposed PBIF method extracts only the low frequency subband of the DWT of a suspicious image. Fixed overlapping square lattices are tiled over each chroma channel of the subband computing a local maximum partial gradient, LMPG, at each pixel location. Consequently, an LMPG image is formed. Local complexity of the LMPG image is subsequently computed at each pixel location. LMPG not only reflects the degree of the DWT coefficient changes, but also de-correlates the image by removing most of the image information except abrupt changes in the pixel values. At the same time, local complexity determines the frequency of the coefficient changes. Appropriate thresholds of the two transition region measures isolate traces of image splicing. The proposed PBIF method is both non-complex and effective in detecting2D image splicing.
The proposed algorithm has a better time complexity than existing algorithms which operate in spatial domain because, initially, the algorithm reduces the dimension of a suspicious image approximately by the factor of the powers of4. Secondly, LMPG reduces computational complexity of the resultant gradient as it is a linear function. Similarly, local complexity reduces the complexity of entropy. Hence both LMPG and local complexity are much less computationally complex operations compared respectively to local resultant gradient (Sobel, LoG) and local entropy which are commonly used in existing methods.
The approach in the designing of all the PBIF methods proposed in this dissertation slightly shifts from the common PBIF design paradigm of absolute detection-rate efficiency to detection-rate-complexity trade-off efficiency. In the former paradigm, the main objective is to design a PBIF method whose detection rate improves on the detection rates of the existing methods. Little attention is given to the complexity of the designed PBIF method. In an effort to finding enabling features for such a design, high computational costs are usually incurred. However, in the latter paradigm the main objective is to design a PBIF method which strikes a trade-off between the high detection rate and the low computational complexity. The design paradigm shift is justifiably necessary because currently more miniature devices with limited computational power are incorporating enabling features to capture, read and display digital images. Should the need arise, these miniature devices would require effective and non-complex automated PBIF solutions to assess the authenticity of the images.
一方面,用户中心和极具吸引力的技术(如Web2.0)的发展导致数字图像的使用、存储和传输不断地增加。如Web2.0提供了用户友好和用户生成的服务,如博客、wiki和社交网络等。因此,每个普通的计算机用户可存储和分发数字图像变得更容易。
另一方面,强大的图像处理工具如Adobe Photoshop等的广泛应用使普通计算机用户篡改数字图像变得更简单、熟练和合理。目前,恶意伪造数字图像的方式多种多样,其中最常见的方式有二维图像区域复制粘贴、二维图像拼接和三维计算机图形渲染。下面我们将从图像篡改的起源开始,介绍上述最常见的篡改类型。
图像篡改或伪造是通过一系列操作来操纵图像内容的行为,严格来讲,这些操作包括对比度增强、去除红眼等非进攻性处理以及对图像区域进行复制或合成的恶意处理。因此，图像篡改暗含着将伪造图像负载的信息来获得无根据的图像。
图像篡改的历史与摄影技术一样的悠久。在过去,可以由专家通过使用彩色喷雾压力枪来进行喷绘,通过特殊染料的合适大小点画笔进行润饰,或通过特种滤光器进行对比度和颜色调整等来创建篡改图像。这是一个相对简单的任务,因为其涉及到的技术在当时并不复杂。
然而,越来越多的免费或易负担的强大图像处理和编辑软件,如Adobe Photoshop、 ACD See、iPhoto等的广泛使用,使图像处理相对比较容易,甚至一个缺乏摄影技巧的人也可以轻易做到。如今,存在许多不可感知地修改数字图像的方式,且大多方式可以轻易被一个计算机的普通用户所掌握。下面我们仅仅以下述最常见的方式为例来说明图像篡改。
常见的图像攻击之一是区域复制：也称之为区域克隆或复制-粘贴图像篡改(CMIF)。同幅图的区域复制篡改,即复制并粘贴图像中的一个部分到同一幅图像中的不同位置。通常,图像篡改操作是为了隐藏图像的一些细节信息,这种情况下将复制背景信息。当然,也可能为了增加更多的细
通常情况下,这样的图像篡改不容易发现,因为他们复制的内容来自同一幅图像,它也可以做的目的是增加更多详情。在这种情况下,使得至少有一个对象被克隆。复制粘贴攻击往往是潜移默化的,因为复制的片段粘贴从而使调色板,噪声元件,动态范围和其他性能与图像的其余部分兼容。此外,攻击者可以利用几何处理的复制片段,从而粘贴和融入他们的目标环境。
另一种常见的篡改类型是二维图像拼接。这也被称为图像合成。在图像拼接伪造中,复制图像的一部分,然后粘贴在相同的图像,或不同图像的不同位置上。涉及至少两个图像的图像拼接伪造。在人眼来看,拼接攻击是不明显的。
为了产生由两个或两个以上的图像拼接区域无缝合成图像,羽化操作经常被使用。羽化操作是悄悄地融入其周围粘贴区域的拼接图像的数学运算。基于羽化操作的拼接图像的明显结果是在色彩空间中的图像是去除伪造的拼接痕迹。然而,在图像中总是有通过拼接产生的不连续界线。一个很好的算法是利用图像篡改来证明不连续性。
第三个最常见的图像篡改是三维计算机图形渲染。在三维计算机图形渲染篡改中,逼真的图像合成具有逼真的颜色和纹理的计算机图形。逼真的图像,给出了多个视图和灯光照片的汇总外观。
因此,自动评估数字图像真实性的图像被动盲取证(PBIF)方法不能不引起广泛的重视。PBIF方法是在没有预先嵌入任何信息如水印或签名的情况下评估数字图像的真实性。针对以下几种图像篡改情况,本文提出了四种新颖的、简单有效的PBIF方法：
(1)由于高信噪比(SNR)的加性噪声或高压缩因子的有损压缩受到较小影响的二维图像区域复制粘贴。
CMIF检测算法最初的任务是确定对于一个给定的图像,无需任何先验知识的复制区域形状和位置是否包含克隆地区。一个最显然的方法来完成这样的任务是详尽的比较每个可能的地区。然而,这样的做法是复杂以成倍的增加。
Fridrich率先提出相对高效的块匹配方法。他们的开创性工作是中离散余弦变换计算作为一个特征代表各个重叠块。类似JPEG的量化离散余弦变换系数,按字典顺序排序比较有效的相似性。尽管如此,该技术存在鲁棒性不够和复杂度较高的缺点。
利用类似的方法,Popescu提出了在尺寸上通过PCA表示每个重叠区块来减少特征向量。该技术大大降低了计算的复杂度。它也被证明是强大的轻微像素变化对于加性噪声和有损压缩。由于大多数这些方法是在空间域中操作,它们是计算复杂度还是较高。
针对这类篡改,本文提出了一种PBIF解决方案：首先对整幅可疑图像进行离散小波变换(DWT),提取图像的低频子带^DWT是必须的，以对图像进行降维。
DWT将任何给定的图像分解成四个子带。低频子带包含了图像的大部分能量。因此，近似的图像尽可能和图像特征和其他结构有关。第二个子带给出了更多的细节信息，这些与水平方向上像素之间的差异有关。因此，当需要图像的水平方向梯度信息时，该子带图像是非常有用的。第三个子带提供了更多的细节信息，这与在垂直方向的像素之间的差异有关。因此，类似于第二个字带，该子带图像在需要垂直梯度信息时非常有用。第四个子带提供了与对角线方向像素之间的差异有关的细节信息。因此，与第二、第三子带类似,此子带在需要对角线图像梯度信息时非常有用。只有通过提取低频子带来维持大部分的图像信息，同时极大地减少了图像的空间。这就是为什么本文提出的算法在最初计算可以图像的DWT来减少图像的维度。一个固定大小的窗口按像素逐步在子带上滑动，提取特征向量在每个像素位置。
主成分分析-特征值分解（PCA-EVD),对所提取的特征进行之前，他们与字典排序的效率比较相似。
主成分分析（PCA),是众所周知的多元分析技术。主成分分析法的核心思想是降低有大量相互联的变量的数据集的维数，同时尽可能保留原始数据变化情况。PCA的涉及描述了一系列互不相关的变量方差的线性组合。第一主成分是单位长度具有极大的总体方差的变量的线性组合。第二主成分是线性组合的所有单位长度的线性组合不相关的第一主成分之间的最大差异。未来最大方差不相关变量的线性组合，如继续扫描，直到尽可能保留原始数据变化。
对于一个半正定对称矩阵，发现主要成分是计算求解特征值特征向量的问题相同。PCA-EVD影碟不仅降低了特征向量的维数，而且还消除小波系数的微小变化。
位移向量方法是用来筛选出与可能是复制区域无关的匹配块。移位向量是一个有序对,反映了从原始图像中的对象或区域到复制和粘贴对象或区域间位置坐标的差异。这里的主要思想是，一个可能的重复区域可能包括复制是相互关联的，而不是几个小型的，孤立块小块。因此地方重复做，我们期望有相同的位移量转变的若干小块。因此，这些块将具有相同的位移向量。这是转移载体的核心目的。它也被称为移位向量过滤财产。它过滤了所有那些转移向量不会累积到一个数量大于预定阈值相似的块。
提出的PBIF法不仅简单，而且对SNR高于24dB的加性噪声和质量因子大于70的JPEG压缩的弱攻击是鲁棒的。据观察，重复区域的大小会影响检出率，尤其是在图像JPEG压缩以及加性高斯噪声的图像。如果重复区域的大小是那么大，一般的结果提供更好的检出率。同样，如果JPEG压縮的质量是高的，结果表明较高的检出率。同样，如果信噪比高，检出率也高。在篡改图像处理后，通过未修改的情况下，算法的检测率是100%，为各种规模大于扫描正方晶格的大小，复制的区域。此外，该方法的主要步骤用一幅简单图像作为简化示例进行了说明，增强了对算法的理解。
(2)由于低信噪比(SNR)的加性噪声或低压縮因子的有损压缩受到较大影响的二维图像区域复制粘贴
我们的重点仍然是二维区域克隆或CMIF。然而，我们打算加强CMIF检测，甚至在很大程度上受到额外噪音或有损压缩影响信号的图像。在文献中，各种强大的被动法医方法最近被设计到检测区域克隆的图像操作,只有那些使用块特性方法提取的特征向量的方式在他们最近的算法中介绍。
Luo提出了一个定义7元组特征向量代表各个重叠块的方法。每个特征向量的前三个组件组成整个块的平均值，合并在过去的四个组成部分是块在不同的划分方向，表示整个块的总和的一半金额比率。由于特征向量的组成部分是浮点数，使用字典排序的特征向量排序。该算法的缺点是，它在空间域使用悬而未决的整体图像运作。第二个弱点是字典排序方法。因此，该方法计算复杂。
类似的方法，Lin定义一个9维特征向量，其组件也基于像素强度统计。此外，通过底层操作，组件被四舍五入为整数.因此基数排序是用来排序的特征向量。然而，该算法适用于空间域的整体形象。所以，该算法仍然是复杂的。
主要受加性噪声或有损压缩影响的图像复制粘贴区域只能通过PBIF方法提取更鲁棒的特征来进行检测。本文提出的解决方案通过采用基于块特征（BC)的方法进行特征提取。首先，对整幅可疑图像进行DWT变换，并只提取低频子带；然后采用基于块特征的方法提取特征。
虽然一般DWT中的微小变化将被PCA-EVD截断所删除，在前面的算法中提取的载体功能，仍然受到像素变化影响。因此建议PBIF算法在篡改图像的JPEG压缩或加性高斯噪声影响的情况下降低重复区域的重复检出率。
针对这个缺点可能的解决方案是使用其组件为归一化系数块特征的DWT系数的特征向量，而不是个别的DWT系数。聚类成块的系数具有量化效果，从而使特征向量对像素变化不够敏感。
基于算法所提取的特征的BC，具有更强的诱导量化效果。因此，相对于那些其组件为单一系数或者由可疑的图像经过DWT变换得到的系数，它更具有鲁棒性。
采用基数排序法对提取的特征向量进行字典排序比较它们的相似度。当它对所提取的特征进行排序，基数排序采用整数键。因此，当它被提取的特征向量排序的排序，基数排序算法的计算复杂度远远小于字典，因为字典排序使用浮动键，并采用位移矢量过滤那些孤立的匹配块。
基于BC的方法不仅比基于PCA的方法速度快，而且对加性噪声和JPEG压缩更加鲁棒。图像复制区域在添加信噪比只有20dB的加性噪声或进行质量因子为40的JPEG压縮后也能被检测出来，且准确率高达95%。
据观察，重复区域的大小会影响检出率，尤其是在图像JPEG压缩以及加性高斯噪声的图像。如果重复区域很大，一般的结果提供更好的检测率。同样，如果JPEG压缩的质量很高，则检测率也很髙。同样，如果信噪比高，检测率也高。在后处理未修改的篡改图像的情况下，如果重复区域的尺寸比扫描正方晶格大，算法的检测率可达到100%。
由于用于算法中的基于特征向量的每个BC包含量化组件，所以算法的一方面是对加性噪声和有损压缩具有更强的鲁棒性。此外，也可能是因为特征向量是使用快速基数排序而不是词典排序，使得算法具有较低的复杂度。
然而，算法的另一面是，在检测重复区域时其抗旋转和缩放性，继承了移位向量的不足。己提出的一个特设的伪自动方法来处理这个问题。但它不能被视为一个完整的解决问题的方法，因为它不是全自动的。这个缺点可能的解决方法是使用验证的方法来代替移位向量方法，这种方法对重复区域的几何处理不够敏感。因此，这些能够克服移位向量的缺点的算法是很必要的。
(3)复制区域经过仿射变换操作（如翻转、旋转、縮放）的2D图像区域复制
图像的几何形变操作的需要，在2D图像处理中很常见。其目的是消除由相机引起畸变和复制攻击的痕迹。这种简单的几何操作存在于大多数现有的图像复制检测方法中，尤其是那些移矢量管行无法准确检测的复制。
如果特征提取和验证或者选择方法对于几何操作鲁棒，那么PBIF方法就能检测这种复制。因此，在这些特征向量的提取阶段和验证阶段的复制区域的几何操作是不敏感。
本文设计的具有几何鲁棒性的新PB1F方法提取了基于块特征的仿射不变特征。为有效比较特征向量的相似度，用基数排序法对特征向量进行字典排序。
选择方法部分源于以下的技术进步。Amerini曾一度提出恢复图像中的仿射变换参数或一个区域的几何操作的想法。在他们的研究中，作者利用单应性的最大似然估计在同质坐标仿射变换参数。
然而，Christlein提出一个简单、直接恢复图像在二维笛卡尔坐标的仿射变换参数的方法，叫做SAT (相同仿射变换选择)。SAT是一种常见的替代选择和验证，用于矢量移位的方法。像移位矢量，SAT拥有的离群过滤的特性。但是，SAT是不敏感的仿射变换，它恢复几何操作区域的仿射变换参数略有增加计算的时间成本。因此，SAT更适合用于图像区域经过平移，旋转或缩放的图像区域复制。
同时，采用相同的仿射变换选择（SATS)方法来过滤孤立的匹配块。与位移矢量不同，SATS对几何篡改攻击是不敏感的。本文提出的PBIF方法能有效并高效的检测出经过仿射变换的篡改区域。值得注意的是，结果表明，该算法的平均推荐精度的情况下重复区域仅仅是翻译或反射。在高检测率也登记的情况下重复区域受到JPEG压缩或附加噪声。重复区域的仿射变换的旋转或联合形式的影响是一个值得推荐的准确性。重复的区域受到缩放或者旋转任意角度，该算法的准确度将稍微降低。较低的检测结果是由于局部发生时通过缩放或旋转任意角度的重复侵袭的区域是由固定的窗口扫描的像素交流。然而，在一般情况下，我们注意到，该算法的准确性随着重复区域大小的增加而增加。
从结果可以看出，该算法优于现有的算法，尤其是在精度和计算复杂性方面。它也优于SATS的这些算法在计算复杂性方面的管道，因为不像大多数悬而未决可疑图像在空间域上运行这种算法，该算法在较低频率的小波子带运行。SATS的唯一算法，提出了由Christlein个人小波系数表现不佳（127检测可能重复2588)。相比之下，我们的算法在执行BC上的小波系数的SATS，具有更高的精度。
其他算法的优势，包括每个BC基于特征向量提取的算法，由于他们是由通过量化块平均和下截断操作组成的，更强大的加性噪声和有损压缩算法。此外，因为使用更快的基数排序，而不是字典排序进行排序的特征向量，使得该算法具有较低的复杂度。最后，由于特征向量不变仿射变换和仿射变换的弹性重复验证方法，选择相同的仿射变换使用，该算法更强大的仿射变换。
然而，该算法的弱点之一是其检出率下降情况下，旋转任意角度，而不仅仅是九十度的倍数。这是因为，大部分像素保持在这种旋转的同心广场，一些像素仍然丢失在过程中将会获得新的像素。局部的像素交换上的微小差异而引发的组件可能逃脱的量化效果和地面操作的平均。这个弱点可能的解决办法是设每个同心圆有一个多边形的侧面的数量大于4，只考虑那些像素子块内的多边形。我们认为，更大的边数将会达到更好的检测精度。在不久的将来，我们将探讨可能的解决方案。
另一个弱点是，该算法仅适用于特定类型的图像在相同的图像伪造复制的粘贴。因此，需要有以下算法来解决该问题。
(4)两幅或更多幅图像的2D图像拼接
数字图像拼接伪造是一个特定类型的图像篡改。在图像拼接伪造中，复制图像的一部分，然后粘贴在相同的图像，或在不同的图像的不同位置上。一个拼接图像可能会有或没有拼接后处理，如羽毛操作。在这两种情况下，由拼接过程中引入的工件可能人眼几乎无法察觉。
许多图像拼接检测方法已经被设计出来。显著的例子包括由Ng提出的在拼接过程中引入的操纵图像的相干高阶矩谱检测尖锐的连续性。然而，该方法的检出率并不令人鼓舞。
另一个突破性的图像拼接检测工作是由Johnson提出的利用不一致的照明检测图像造假技术。这个技术对于检测有着相似照明条件的拼接图像没有效果。此外，它是悬而未决的图像，因此复杂。
图像拼接检测的另一个企图是由陈中的2-D相位一致性和特征函数的统计矩雇用卡车拼接故事。然而，特征提取的过程非常耗时。此外，检出率并不反映涉及的努力。
检测图像拼接的基础上，灰度共生矩阵是从图像中提取二阶纹理信息知名的代表性。在色度空间的边缘图像的灰度级共生矩阵进行了研究。应用在拼接文物通常不被伪装的可疑图像的色度空间的方法，提高了算法的有效性。然而，尽管执行的特征尺寸减少，方法通常是复杂的。例如，它使用巨大的特点和悬而未决的可疑图像在空间域上进行。
本文提出的PBIF方法首先提取可疑图像经DWT后的低频子带，然后用固定大小的重叠方块平铺在各个颜色通道的子带，并计算每一个像素位置的局部极大梯度（LMPG)，得到了LMPG图像。随后计算LMPG图像每一个像素点的局部复杂度。LMPG不仅反映了DWT系数变化的程度，还通过消除除像素值突变外的大部分图像信息来对图像进行去相关。同时，局部复杂度决定了系数变化的频率。两个过度区域的适当阈值估计了图像拼接中的孤立痕迹。该PBIF方法能简单有效的检测2D图像拼接篡改。
该算法具有较好的时间复杂度比现有的算法在空间领域的运作，最初.因为该算法减少4的权力因素可疑图像的尺寸约。其次，LMPG减少所造成的梯度计算复杂性，因为它是一个线性的数量。同样，本地的复杂性，降低熵的复杂性。因此双方LMPG和地方的复杂性要少得多，分别比本地产生的梯度（Sobel算子，LOG)和地方在现有的方法，通常使用的熵计算复杂的操作。
在追踪连续性拼接的方法，检测的基本假设推出时，两个或两个以上的图像区域缝合在一起，这种不连续性有尖锐的过渡。一个正常的过渡区域，如对象的边缘或边界将有相对大量的像素之间的过渡梯度分布。这意味着，这些地区将不会有一个单独的像素位置高梯度，但显然有较高的局部熵或当地复杂。伪装后处理操作，如羽毛操作拼接地区模仿正常的物体边缘或边界的素质。未经处理后拼接地区将有高梯度和更低的复杂度或熵的过渡，因为被分配到一个像素的数量有限。
根据基本的假设，很显然，LMPG和局部复杂度提供了足够的信息来区分这些不连续性，同时避免了度量产生的梯度和熵时的计算复杂度。在提出的算法中所描述的LMPG和局部复杂度的合适阈值隔离了拼接的痕迹，其有效性就如由阈值所分别生成的梯度和熵，但具有较高的时间效率。此外，如果选择色度空间，将提高检测精度，因为拼接的痕迹在这样的空间中是公开的。
我们必须强调的是我们建议该提出的算法在YCbCr色彩模型的色度空间中进行。它是建立在这样基础上，一般情况下大多数图像拼接和图像操作都是在RGB色彩空间中进行的。因此，有很大的可能性使得拼接攻击将在R，G和B这样的颜色通道中被很好地掩盖起来。而当图像操作受其他色彩模型如YCC或YCbCr等的影响时，注意力将主要放在了亮度通道中。这时极有可能使拼接痕迹在Y通道中不明显而在色度通道中非常明显。在色度空间中的这种一般的忽视将会错过揭示色度空间中的篡改信息。
最近的研究工作表明如果将单一颜色模型下的一幅不可感知拼接的图像转换到其他颜色模型下，那么该图像将揭露出篡改的信息。特别是，在RGB色彩空间中无明显拼接痕迹的图像，当从YCbCr色彩模型的色度空间进行检测时将显示出拼接的痕迹。因此，为了获得更好的检测结果，我们建议在色度空间执行该提出的算法。这并不一定意味着该算法无法检测到在RGB色彩空间或亮度-色度颜色空间的Y通道中进行的拼接操作。
该算法的主要优势在于局部复杂度的降低以及通过为LMPG选择花销更低的迁移权值降低了计算复杂度。另外，实际上，实现图像拼接检测相当于在一个强图像内容信号中检测弱信号存在性的任务。该算法聚焦在色度空间中是有利的，这是因为图像内容构成的强信号将因为拼接不连续而被删除来保持目标弱信号。因此，通过颜色模型之间的图像转换’拼接检测任务减轻了存在于另一个弱背景信号中弱信号检测的难度。伪自适应阈值胜过了这一优势。该算法的性能非常不错，其整体结果的精度约为82°/^
然而，该算法仍然面临着许多挑战。例如，伪自适应阈值，尽管他们优于大多数现有方法中采用的阈值，但仍尚未完全自适应。在不久旳将来，我们将进行广泛搜索来获得自适应的阈值。此外，在经受后拼接处理和正常的图像边缘处理后的拼接不连续性间的界线是非常敏锐的，以致本文提出的算法能很容易地阻止误报。“个可能的解决方案是在证实最终检测结果之前，在中间检测结果上融入一个合适的保真度值。在未来的工作我们将重点研究如何获得这个保真度值。
一般的PBIF设计方案偏向考虑检测率的有效性，在本文中提出的PBIF方法在设计中则偏向考虑检测率的有效性和复杂度的折中。前一种设计方案的主要目的是设计一个比现有方法检测率有所提高的PBIF方法，但极少关注设计的PBIF方法的复杂度。为提高检测率寻找可行的特征经常会导致高的计算成本。然而，本文的设计方案的主要的目标就是设计使高检测率和低计算复杂度协调一致的PBIF的方法。这个设计方案目标的转变是合理而必要的，因为现在更多计算能力有限的微型设备融合了捕获、读取和显示数字图像的能力。随着需求的增加，这些微型设备要求使用有效而简单的自动PBIF方法来评估图像的真实性。
In this digital era, digital visual media represent one of the principal means of communication. Digital videos and images have coherently become the main information carriers. Currently, the mainstream media, courtroom exhibits, fashion magazines, scientific journals, political campaign tools and the internet are all experiencing increasing usage, hosting and transmission of digital visual data in form of digital images. However, the reliability of digital images has lately been questioned mainly because the visual imagery is experiencing contradicting technological developments. On one hand, the development of user-centric and attractive technologies such as Web2.0, which provide user-friendly and user-generated facilities such as blogs, wikis, and social networks, has resulted in increased usage, hosting and transmission of digital images. It has become more affordable to an average computer user to store and distribute digital images.
On the other hand, the availability of powerful image processing tools such as Adobe Photoshop has enabled the average computer user to doctor digital images with increasing ease and sophistication. Currently, there are numerous ways of maliciously forging a digital image. Among the most common ways of digital image forgery are the2D region duplication,2D image splicing and3D computer graphic rendering. We start by briefly describing these common kinds of image attack as follows
Region Duplication:This is also known as a region cloning or copy-move image forgery, CMIF. In a region duplication forgery, a part of an image is copied and then pasted on a different location within the same image. Usually, such an image tampering is done with the aim of either hiding some image details, in which case a background is duplicated. It may also be done with the aim of adding more details in which case at least an object is cloned. Only one image is involved in the region duplication forgery.
2D Image Splicing:This is also known as image compositing. In an image splicing forgery, a part of an image is copied and then pasted on a different location within the same image or in a different image altogether. At least two images are involved in image splicing forgery.
3D Computer Graphic Rendering:In a3D computer graphic rendering forgery, a photorealistic image is synthesized from augmenting a computer graphic with color and texture. The photorealistic image is given an aggregated appearance of photographs of multiple views and lightings.
It is clear that the need for passive and blind image forensics, PBIF, methods to automatically assess the authenticity of the digital images cannot be overemphasized. PBIF methods assess the authenticity of digital images in absence of embedded schemes such as watermarks or signatures. In this dissertation, we propose four new, effective and non-complex PBIF methods as automated solutions to the following image tampering:
(1)2D region duplications in which the duplicated image regions have been affected by minor variations due to additive noise or lossy compression where both signal-to-noise ratio, SNR, and compression quality are high;
The primary task of a CMIF detection algorithm is to determine if a given image contains cloned regions without having any prior knowledge of the shape and location of the copied regions. An obvious approach to accomplishing such a task is to exhaustively compare every possible pair of regions. However, such an approach is exponentially complex.
The proposed PBIF solution firstly performs the discrete wavelet transform, DWT, of the whole suspicious image and extracts only the low frequency subband to approximate the image. DWT is necessary to reduce the dimension of the image. A fixed size window is then slid over the subband, pixel by pixel, extracting a feature vector at each pixel location.
Principal component analysis-eigenvector decomposition, PCA-EVD, is subsequently performed on the extracted features before they are lexicographically sorted for efficient comparison for similarities. Principal Component Analysis, PCA, is a well known technique for multivariate analysis. The core idea of PCA is to reduce the dimensionality of a data set which has a large number of interrelated variables, while retaining the original variations of the data as much as possible. In addition, PCA-EVD not only reduces the dimension of the feature vectors, but also removes minor variations in DWT coefficients.
Shift vector approach is used to filter out matching blocks which are not connected into possible duplicated regions. A shift vector is an ordered pair of the differences in the coordinates of the positions or locations of a copied and relocated object or region in an image from the original object or region in an image.
The proposed PBIF method is not only non-complex, but also robust to weak attacks of additive noise as along as SNR is above24dB and JPEG compression as long as the quality is above70.
Furthermore, the major steps of the proposed PBIF algorithm are illustrated through a simplified example involving a toy image, which enhances the explanation of the algorithm.
(2)2D region duplications in which the duplicated image regions have been affected by major variations due to additive noise or lossy compression where both SNR and compression quality are low;
Our focus is still on detecting2D region cloning or CMIF. However, we intend to enhance detection of CMIF even in images where the signal is heavily affected by additive noise or losy compression.
Duplicated regions which are affected by major variations due to additive noise or lossy compression can only be detected by PBIF methods which extract more robust features. In the proposed solution, a block characteristic, BC, based approach to feature extraction is taken. Initially, DWT of the whole suspicious image is performed and only the low frequency subband is extracted to approximate the image. BC based features are extracted.
The BC based features which are extracted in this algorithm have more induced quantization effect. Therefore, they are more robust to attacks than the feature vectors whose components are individual pixels or coefficients of DWT transform of the suspicious image.
Radix Sort is used to lexicographically order the feature vectors for efficient comparison for similarities. Radix sort uses integer keys when it is sorting the extracted features. As a result, the computational complexity of the Radix sort algorithm is much smaller than that of the lexicographical sort because lexicographic sort uses floating keys when it is sorting the extracted feature vectors. Shift vector filters out isolated matching blocks.
The BC based method is not only faster than the PCA based method, but also more robust to additive noise and JPEG compression. It is capable of detecting, with efficiency as high as95%, duplicated regions affected by either additive noise where the signal is as weak as20dB, or JPEG Compression where the quality is as low as40.
(3)2D region cloning in which the duplicated image regions have been affected by affine transformation operations such as reflection, rotation, or scaling;
It is not uncommon for an image attacker to reflect a duplicated region, rotate the region through an arbitrary angle or scale the region, in addition to translating it. A PBIF method can only detect such duplications if both the extracted features and the verification or selection method are robust to those geometric operations. The designed new geometrically robust PBIF method extracts BC based features that are invariant to affine transformation. Radix Sort is subsequently used to lexicographically order the feature vectors for efficient comparison for similarities.
At the verification stage, a same affine transformation selection, SATS, is used to filter out isolated matching blocks. Unlike shift vector, SATS is insensitive to geometric attacks. The proposed PBIF method effectively and efficiently detects regions affected by affine transformation
(4)2D digital image splicing, a kind of forgery in which two or more images are involved.
A digital image splicing forgery is a specific kind of image tampering. In an image splicing forgery, a part of an image is copied and then pasted on a different location within the same image or in a different image altogether. A spliced image may be with or without post-splicing processing, such as feather operation. In either case, the artifacts introduced by the splicing process may be almost imperceptible to the human eye.
The proposed PBIF method extracts only the low frequency subband of the DWT of a suspicious image. Fixed overlapping square lattices are tiled over each chroma channel of the subband computing a local maximum partial gradient, LMPG, at each pixel location. Consequently, an LMPG image is formed. Local complexity of the LMPG image is subsequently computed at each pixel location. LMPG not only reflects the degree of the DWT coefficient changes, but also de-correlates the image by removing most of the image information except abrupt changes in the pixel values. At the same time, local complexity determines the frequency of the coefficient changes. Appropriate thresholds of the two transition region measures isolate traces of image splicing. The proposed PBIF method is both non-complex and effective in detecting2D image splicing.
The proposed algorithm has a better time complexity than existing algorithms which operate in spatial domain because, initially, the algorithm reduces the dimension of a suspicious image approximately by the factor of the powers of4. Secondly, LMPG reduces computational complexity of the resultant gradient as it is a linear function. Similarly, local complexity reduces the complexity of entropy. Hence both LMPG and local complexity are much less computationally complex operations compared respectively to local resultant gradient (Sobel, LoG) and local entropy which are commonly used in existing methods.
The approach in the designing of all the PBIF methods proposed in this dissertation slightly shifts from the common PBIF design paradigm of absolute detection-rate efficiency to detection-rate-complexity trade-off efficiency. In the former paradigm, the main objective is to design a PBIF method whose detection rate improves on the detection rates of the existing methods. Little attention is given to the complexity of the designed PBIF method. In an effort to finding enabling features for such a design, high computational costs are usually incurred. However, in the latter paradigm the main objective is to design a PBIF method which strikes a trade-off between the high detection rate and the low computational complexity. The design paradigm shift is justifiably necessary because currently more miniature devices with limited computational power are incorporating enabling features to capture, read and display digital images. Should the need arise, these miniature devices would require effective and non-complex automated PBIF solutions to assess the authenticity of the images.
引文
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