基于彩色LBP的隐蔽性复制-粘贴篡改盲鉴别算法

现有的复制-粘贴盲鉴别算法大多忽略图像彩色信息,导致对隐蔽性篡改方式的检测率较低,基于此,本文提出一种基于彩色局部二值模式（Color local binary patterns,CoLBP）的隐蔽性复制-粘贴盲鉴别算法.算法首先对彩色图像进行预处理,即建立彩色LBP纹理图像,从而实现彩色信息与LBP纹理特征的融合;其次重叠分块并提取灰度共生矩阵（Gray level co-occurrence matrix,GLCM）特征;最后,提出改进的kd树和超平面划分标记split搜索方法,快速匹配图像块,并应用形态学操作去除误匹配,精确定位复制-粘贴区域.实验结果表明,本算法对隐蔽性复制-粘贴篡改定位准确,并对模糊、噪声、JPEG重压缩后处理操作有很好的鲁棒性.     

基于LBP的图像区域复制篡改检测

研究尺度不变特征变换(SIFT)和旋转不变局部二值模式(LBP)相结合的特征匹配方法,提出一种基于LBP的图像区域复制-粘贴篡改检测算法。利用SIFT关键点检测方法检测图像中的所有关键点,计算以关键点为中心的周围图像区域的LBP特征,并将其作为关键点的特征描述,采用特征向量的欧式距离进行关键点匹配。实验结果表明,该算法在抗旋转、亮度变化处理和效率方面均优于基于主成分分析的检测算法。?     

基于Krawtchouk不变矩的复制-粘贴篡改盲检测算法

针对数字图像检测中一类常见的复制-粘贴图像篡改,提出一种基于小波变换和径向Krawtchouk不变矩的盲检测算法。算法利用小波变换提取图像的低频分量,对低频分量分块提取径向Krawtchouk不变矩特征,这种特征描述方式对图像旋转后处理具有鲁棒性,然后将特征向量进行按字典排序,并结合数学形态学进行图像复制篡改区域的检测和定位。实验表明该算法能有效地定位出复制和粘贴的图像篡改区域,并对粘贴区域旋转操作具有很强的鲁棒性。     

基于分形和统计的复制-粘贴篡改图像的检测

现在大多数图像“复制-粘贴”篡改检测算法对于区域复制后的进一步混合处理不能进行有效检测。为此提出了一种新的基于分形和统计的检测方法。首先将图像分块并提取每块的特征向量,该特征向量由分形维数和三个统计量组成;接着对所有特征向量进行字典排序;最后,利用图像块的位置信息和欧氏距离定位篡改区域。此方法不仅能够检测传统的复制-粘贴型篡改,而且还能够检测经过旋转、翻转以及旋转和翻转混合的多区域复制-粘贴型篡改;此方法也能够抵抗高斯模糊、对比度调整和亮度调整等攻击。实验结果表明了该方法的有效性。     

基于SURF特征匹配的复制—移动篡改区域探测方法

针对复制-移动篡改,本文提出基于SURF(Speeded Up Robust Features)特征匹配篡改区域快速自动探测与定位方法。首先应用SURF算法提取待检测图像的特征点和特征向量并进行特征匹配,然后估计匹配对之间的仿射变换参数并消除错配,最后通过仿射变换找出趋近完整的复制-移动区域。实验结果证明了该方法对复制-移动篡改探测的有效性。     

基于几何均值分解和结构相似度的同源视频时间域复制粘贴篡改快速检测及恢复方法

针对现有方法中篡改检测效率不高、定位不精确的问题,提出了一种基于几何均值分解(GMD)和结构相似度(SSIM)的同源视频复制-粘贴快速篡改检测及恢复的方法。首先,将视频转换为灰度图像序列。其次,将几何均值分解作为检测特征,提出了一个基于块的搜索策略来定位复制序列的起始帧。此外,算法首次将结构相似度用于度量视频两帧之间的相似度,并利用结构相似度对搜索策略得到的起始帧进行复检。由于复制视频序列对应两帧之间的相似度高于未篡改序列对应两帧之间的相似度,提出了一个基于结构相似度的从粗到精的方法来定位复制视频序列的末尾帧。最后,对视频进行恢复。与其他几种经典算法进行对比,实验结果表明,所提方法不仅能够检测经过复制-粘贴篡改操作的视频,而且能准确地定位复制-粘贴序列。此外,该方法在检测精度、召回率和检测时间上有较大提升。     

基于投影数据主成分分析的图像篡改检测算法

传统算法处理图像复制-粘贴型篡改问题时速度较慢。为此,提出一种基于投影数据主成分分析(PCA)的图像篡改检测算法。利用分块图像的行、列投影构建图像块投影特征矩阵,通过PCA对其降维,并使用字典排序法进行排序,结合图像块偏移置信距离判断图像复制-粘贴区域,完成被动取证。实验结果表明,该算法能准确找出篡改区域,与Posucue算法相比速度较快。     

基于LBP的图像复制篡改检测

针对比较常见的图像的复制-粘贴篡改技术,提出一种基于局部二值模式LBP(local binary pattern)的检测算法。首先把需要检测的已经被篡改的图像分成大小相同的重叠块,每块的纹理特征用LBP(旋转不变)向量去表示,从而得到被检测图像的特征矢量;然后对得到的特征矢量进行字典排序,并结合检测图像块的位移矢量,准确定位并检测出图像中的被篡改区域。实验结果表明:在抗旋转处理和效率方面该算法均优于经典的基于PCA的检测算法。     

基于点匹配的区域拷贝篡改检测

图像区域拷贝是一种常见的数字图像篡改技术,目前的大部分数字图像区域拷贝取证技术未考虑旋转和缩放因素。提出一种新的基于点匹配的图像区域篡改检测算法。首先利用尺度不变旋转变换(SIFT)寻找图像中的关键点,使用主成分分析法(PCA)对关键点进行降维描述,然后利用关键点特征向量的相似度寻找关键相似点。实验表明,该算法不但能够较精确地定位出复制和粘贴的图像伪造区域,还能有效抵抗噪声污染、有损压缩以及旋转等攻击,并有效地减少运算量,提高了检测效率。     

利用Harris特征点和环形均值描述的图像区域复制篡改的被动取证

提出一种利用Harris特征点和环形均值描述的图像区域复制篡改检测算法。首先对图像进行自适应维纳滤波,并利用Harris算子提取图像的特征点,然后通过对每个特征点的环形邻域进行均值描述生成特征向量矩阵,并采用字典排序和阈值化处理进行相似性匹配,从而确定候选匹配点,最后利用RANSAC算法剔除错误的匹配点,实现复制和篡改区域的标识定位。实验结果表明,算法对于复制区域的旋转和翻转变换具有较强的鲁棒性,并且可以有效抵抗常见的后处理攻击,包括高斯模糊、加性高斯白噪声、JPEG压缩以及它们的混合操作,尤其能够抵抗非显著视觉结构的平坦区域和小区域的复制、粘贴、篡改操作。     

A new keypoint-based copy-move forgery detection for small smooth regions

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.     

Accurate and robust localization of duplicated region in copy–move image forgery

Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.     

基于区域划分和四元数的彩色图像复制粘贴篡改检测

现有的篡改检测方法中特征点提取不充分会导致篡改检测精度不高,特征点描述符识别率差,针对该问题提出一种基于颜色矩的区域划分和四元数Hu矩的彩色图像复制粘贴篡改检测算法。首先,使用自适应形态重建算法对图像进行超像素分割,通过密度聚类算法对图像自适应划分区域;其次,提出一种关键点提取方法得到均匀的SIFT特征点;然后,在一种新颖的彩色图像四元数表示方法中构建局部高斯金字塔提取Hu矩特征;最后,利用2NN进行特征匹配后,结合Delaunay三角形算法定位出复制粘贴篡改区域。在公共数据集上的实验结果表明,该算法可以更有效地定位篡改区域。     

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image forgery detection remains a challenging problem. For the most common copy-move forgery detection, the robustness and accuracy of existing methods can still be further improved. To the best of our knowledge, we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network (PCNN) and the self-selected sub-images. Our method has the following steps: First, contour detection is performed on the input color image, and bounding boxes are drawn to frame the contours to form suspected forgery sub-images. Second, by improving PCNN to perform feature extraction of sub-images, the feature invariance of rotation, scaling, noise adding, and so on can be achieved. Finally, the dual feature matching is used to match the features and locate the forgery regions. What's more, the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction, and the improved PCNN can extract image features with high robustness. Through experiments on the standard image forgery datasets CoMoFoD and CASIA, it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method, which is a more efficient image copy-move forgery passive detection method.     

一种基于自适应闭值的图像伪造检测算法

图像伪造检测是数字取证领域一个发展迅速的研究方向。复制一移动是最常见的图像伪造方式之一,其目的是通过隐藏或克隆对象来创建新的图像内容场景。复制一移动伪造检测的主要依据是图像中存在较大面积的相同或非常相似的区域对。针对以往检测方法对图像中存在同质纹理或均匀区域检测困难以及相关参数阂值选择不确定等现状,提出一种基于自适应阂值的图像复制一移动伪造检测算法,该算法不但使相关阂值的选择和估计更合理,而且能够自动识别和定位伪造区域。通过在包含同质或均匀区域的彩色伪造图像中的实验,进一步验证了本算法的有效性。     

基于DeepLab v3+的多任务图像拼接篡改检测算法

在图像拼接篡改检测任务中,受篡改区域尺度多样性及模糊操作的影响,传统分类算法难以提取图像篡改特征。提出一种基于DeepLab v3+的图像拼接篡改检测算法,使用浅层图像特征预测图像的篡改区域边界,提高模型对篡改边界的敏感性。在此基础上,通过多尺度融合特征对图像篡改区域进行分割,并在原空洞空间金字塔模块中融合空间和通道注意力机制,从而提高模型对多尺度篡改区域的适应性。实验结果表明,所提算法能有效检测图像的篡改区域,在CASIA v1.0和Columbia数据集中的分割精度分别为0.754 6和0.727 8,优于DCT、BAPPY、MFCN等算法。     

基于改进显著图和局部特征匹配的copy-move窜改检测

检测整幅窜改图像的方法增加了许多非必要的计算量,为了降低计算复杂度和进一步提高检测精确率,提出了一种基于改进显著图和局部特征匹配的copy-move窜改检测方法。首先,结合图像梯度改进显著图,分离出包含图像高纹理信息的局部显著区域;其次,只对该局部区域采用SIFT(scale invariant feature transform)算法提取特征点;然后,对显著性小的图像采用密度聚类和二阶段匹配策略,对显著性大的图像采用超像素分割和显著块特征匹配的策略;最后,结合PSNR和形态学操作来定位窜改区域。在两个公开数据集上进行实验,该方法的平均检测时间小于10 s,平均检测精确率大于97%,均优于所对比的方法。实验结果表明,该方法能够大幅缩减检测时间、有效提高检测精确率,并且对几何变换和后处理操作也都具有较好的鲁棒性。     

基于超像素形状特征的图像复制粘贴篡改检测算法

针对传统图像复制粘贴篡改检测方法中划分子块的数目过大导致算法时间复杂度过高且抵抗几何变换能力较弱的问题,提出一种基于超像素形状特征的图像复制粘贴篡改检测算法.首先提出基于小波对比度自适应划分超像素的方法分割图像并提取稳定的特征点;然后提出新颖的形状编码方式提取超像素形状特征,并与特征点融合,估计可疑伪造区域;最后对可疑...