Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.

Image Copy-Move Forgery Detection Using SURF in Opponent Color Space

Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.

Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques

Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.

A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed.

Efficient Forgery Detection Approaches for Digital Color Images

This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.

Detection of Copy-Scale-Move Forgery in Digital Images Using SFOP and MROGH

Social network platforms such as Twitter, Instagram and Facebook are one of the fastest and most convenient means for sharing digital images. Digital images are generally accepted as credible news but, it may undergo some manipulations before being shared without leaving any obvious traces of tampering; due to existence of the powerful image editing softwares. Copy-move forgery technique is a very simple and common type of image forgery, where a part of the image is copied and then pasted in the same image to replicate or hide some parts from the image. In this paper, we proposed a copy-scale-move forgery detection method based on Scale Invariant Feature Operator (SFOP) detector. The keypoints are then described using MROGH descriptor. Experimental results show that the proposed method is able to locate and detect the forgery even if under some geometric transformations such as scaling.

基于小波和不变矩的图像copy-move篡改盲检测

针对数字图像取证中一类常见的复制粘贴图像伪造,本文提出了一种基于小波变换和不变矩提取的检测算法。该算法利用小波变换提取图像的低频分量,对低频分量分块进行不变矩特征提取,然后将特征矢量进行按行字典排序,并且配合图像块的偏移位置信息,进行图像复制伪造区域的检测和定位。实验表明该算法能够较精确地定位出复制和粘贴的图像伪造区域,并有效地减少了运算量,提高了检测效率。

Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.

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.

基于小波和奇异值分解的图像复制伪造区域检测

针对数字图像取证中一类常见的复制粘贴图像伪造,本文提出了一种基于小波变换和奇异值分解的检测算法.该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奇异值特征,这种特征描述形式对图像羽化或边缘模糊等处理具有鲁棒性.然后将特征矢量进行按行字典排序,并且配合图像块的偏移位置信息,进行图像复制伪造区域的检测和定位.实验表明该算法能够较精确地定位出复制和粘贴的图像伪造区域,并有效地减少了运算量,提高了检测效率.

鲁棒的区域复制图像篡改检测技术

区域复制把数字图像中一部分区域进行复制并粘贴到同一幅图像的另一个区域中,以达到去除图像中某一重要内容的目的,是一种简单而有效的图像篡改技术.现有检测算法对区域复制后处理的鲁棒性较差.文中针对此篡改技术,提出了一种有效的检测与定位篡改区域算法.该算法首先将图像分解为小块并比较各小块间的相似性,最后利用"主转移向量"方法去除错误的相似块对得到篡改的区域.实验数据说明该算法能有效地对抗多种区域复制的后处理操作,包括高斯模糊、加性白高斯噪声、JPEG压缩及它们的混合操作.

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。

基于几何均值分解的图像区域复制篡改检测方法

针对现有大多数图像区域复制篡改检测算法提取图像块的特征向量维数较高的缺点,提出一种新的基于几何均值分解的检测算法.将可疑图像分成大小相等的可重叠的子块;并对每个图像块进行几何均值分解并用其表征该子块的特征,形成1维的特征向量;最后对所有的特征向量进行字典排序,并结合图像块的相等位移矢量的发生频率信息,检测并定位出篡改区域.实验结果表明,该算法不仅能够有效检测并定位多区域复制篡改区域,而且对高斯模糊、对比度调整、曝光度调整的后处理操作具有较强的鲁棒性,并且有效地降低了特征向量的维数,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。

基于TSVD的图像复制区域伪造检测算法

提出了一种有效的盲检测算法来识别图像复制区域伪造。该算法采用截尾奇异值分解(truncated sin-gular value decomposition,TSVD)变换来处理图像块数据,并对图像块进行相似性匹配检测。实验结果表明,本算法具有较强的检测能力,能够有效抵抗多种修饰操作,如JPEG有损压缩、高斯模糊、高斯白噪声污染等。

基于Zernike矩的图像区域旋转篡改检测

区域旋转复制粘贴是极为常见的图像篡改方式之一,为了有效地检测这种图像篡改,该文提出一种基于Zernike矩旋转不变性的篡改检测算法。首先,将图像划分为相互重叠的小块并计算各小块的Zernike矩特征值,对其进行字典式向量排序;其次,计算字典式矩阵相邻行之间的相关系数确定旋转篡改区域;最后,估计区域的旋转角度。实验结果表明,该算法不仅能够精确定位图像中旋转篡改的区域,而且能估算出区域旋转角度。可用于面向图像内容的真实性认证。

联合Harris特征点与Hu不变矩的图像区域复制篡改盲取证

提出了一种将Harris特征点和Hu不变矩相结合的图像区域复制篡改检测方法.首先采用Harris算子检测图像中的角点作为特征点,然后以特征点为中心计算其邻域内Hu矩的前4维低阶矩形成特征向量,最后记录相等位移矢量的发生频率并通过阈值化处理得到匹配的特征点,从而标识复制粘贴区域.仿真实验表明,该算法不仅可以有效检测多区域复制粘贴篡改操作,而且能够有效抵抗多种篡改后处理操作,包括加性高斯白噪声,JPEG压缩,对比度、亮度和曝光度调整以及JPEG压缩和加噪的混合操作.

数字图像的拷贝翻转移动篡改检测

常见的图像篡改方法之一是将一幅图像中的部分内容复制处理后移动到其他位置产生新的伪造目标。针对将复制区域翻转后再润色粘贴的图像篡改提出了一种基于一组翻转不变的特征向量的检测方法。该检测方法不仅能检测一般的复制—移动—粘贴的行为,而且能检测出欺骗性较强的复制—翻转—移动—粘贴的行为,并能抵抗模糊和加入噪声等用来抹除篡改痕迹的常用润色方法和JPEG有损压缩。对几幅典型篡改图像的检测结果说明了算法的有效性。

基于模糊不变矩和区域生长的图像盲检测算法

提出了基于模糊不变矩和区域生长的篡改盲检测算法。该算法根据图像模糊原理,分析复制-粘贴篡改区域的特点,采用24个模糊不变矩表示图像子块特征;通过子块特征的相似度确定篡改区域的侯选种子,再根据侯选种子的邻域块对的相似度找出篡改区域的真种子,并通过中心连接区域生长法从真种子中生长出经过模糊的篡改区域。本算法对粘贴区域被模糊使之融入到背景之中的篡改图有较好的检测性能。

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。