基于LPP和改进SIFT的copy-move篡改检测

图像的复制-粘贴篡改检测是图像篡改检测领域中的重要组成部分。本文基于SIFT算法以及LPP的降维思想,提出了一种新的篡改检测算法。本文在SIFT算法的基础上,使用LPP算法对SIFT算法生成的特征点以及特征向量进行降维。使得传统SIFT算法在实际应用中特征点数目过多、特征向量维数过高等缺陷得到了解决。并使用凝聚型层次聚类算法对相似的特征点进行聚类,完成了对图像复制-粘贴篡改区域的检测。在文章的最后,本文对哥伦比亚大学复制-粘贴图像库里的100张图片进行实验。实验结果表明,不管篡改区域后处理方式是拉伸还是旋转,本文算法都能比传统的SIFT、SURF、PCA-SIFT等算法生成更少的特征点数目和更低的特征向量维度,使得检测效率以及检测正确率得到有效提升。      

Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants

Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 s on four publicly available datasets.     

Copy-move forgery detection and localization by means of robust clustering with J-Linkage

Understanding if a digital image is authentic or not, is a key purpose of image forensics. There are several different tampering attacks but, surely, one of the most common and immediate one is copy-move. A recent and effective approach for detecting copy-move forgeries is to use local visual features such as SIFT. In this kind of methods, SIFT matching is often followed by a clustering procedure to group keypoints that are spatially close. Often, this procedure could be unsatisfactory, in particular in those cases in which the copied patch contains pixels that are spatially very distant among them, and when the pasted area is near to the original source. In such cases, a better estimation of the cloned area is necessary in order to obtain an accurate forgery localization. In this paper a novel approach is presented for copy-move forgery detection and localization based on the J-Linkage algorithm, which performs a robust clustering in the space of the geometric transformation. Experimental results, carried out on different datasets, show that the proposed method outperforms other similar state-of-the-art techniques both in terms of copy-move forgery detection reliability and of precision in the manipulated patch localization.     

Fast copy-move forgery detection algorithm based on group SIFT

Aiming at the high computational complexity of the existing copy-move image forgery detection algorithm,a copy-move forgery detection algorithm based on group scale-invariant feature transform (SIFT) was proposed.Firstly,the simple linear iterative clustering (SLIC) was utilized to divide the input image into non-overlapping and irregular blocks.Secondly,the structure tensor was introduced to classify each block as flat blocks,edge blocks and corner blocks,and then the SIFT feature points extracted from the block were taken as the block features.Finally,the forgery was located by the inter-class matching of the block features.By means of inter-class matching and feature point matching,the time complexity of the proposed copy-move forgery detection algorithm in feature matching and locating forgery region was effectively reduced while guaranteeing the detection effect.The experimental results show that the accuracy of the proposed algorithm is 97.79%,the recall rate is 90.34%,and the F score is 93.59%,the detecting time for the image with size of 1024×768 is 12.72 s,and the detecting time for the image with size of 3000×2000 was 639.93 s.Compared with the existing copy-move algorithm,the proposed algorithm can locate the forgery region quickly and accurately,and has high robustness.     

Copy-move tampering detection using affine transformation property preservation on clustered keypoints

Recent advances in multimedia technologies have made imaging devices and image editing tools ubiquitous and affordable. Image editing done with malicious intent is called as image tampering or forgery. The most common forgery is the copy-move forgery which involves copying a part of an image and pasting it on some other part of the same image. There are many existing methods for such forgery detection, but most of them are sensitive to post-processing and do not detect multiple instances of forgeries in an image. In the proposed approach, affine transformation property preservation of clustered keypoints in the image is used, which includes the tests for collinearity and distance ratio preservation. Our method is also able to detect multiple copy-move forgeries within an image. The proposed method is tested against four image tampering detection datasets, and the results of our method are the best compared to the existing eight state-of-the-art methods in terms of accuracy.     

An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

Copy-move forgery detection (CMFD) is the process of determining the presence of copied areas in an image. CMFD approaches are mainly classified into two groups: keypoint-based and block-based techniques. In this paper, a new CMFD approach is proposed on the basis of both block and keypoint based approaches. Initially, the forged image is partitioned into non overlapped segments utilizing adaptive watershed segmentation, wherein adaptive H-minima transform is used for extracting the markers. Also, an Adaptive Galactic Swarm Optimization (AGSO) algorithm is used to select optimal gap parameter while selecting the markers for reducing the undesired regional minima, which can increase the segmentation performance. After that, the features from every segment are extracted as segment features (SF) using Hybrid Wavelet Hadamard Transform (HWHT). Then, feature matching is performed using adaptive thresholding. The false matches or outliers can be removed with the help of Random Sample Consensus (RANSAC) algorithm. Finally, the Forgery Region Extraction Algorithm (FREA) is utilized for detecting the copied portion from the host image. Experimental results indicate that the proposed scheme find out image forgery region with Precision = 92.45%; Recall = 93.67% and F1 = 92.75% on MICC-F600 dataset and Precision = 94.52%; Recall = 95.32% and F1 = 93.56% on Bench mark dataset at pixel level. Also, it outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation.     

区分来源和目标区域的图像copy-move伪造检测方法

Copy-move是一种常用的图像伪造手段,它通过复制图像的某一区域,移动并粘贴到同一图像的其他位置,达到掩盖重要信息或伪造虚假场景的目的。近年来,为了防止copy-move被用于违法犯罪,copy-move伪造检测技术迅猛发展,在维护社会运行秩序和信息安全方面发挥着积极作用。本文提出一种基于条件生成对抗网络(conditional Generative Adversarial Networks, cGANs)的copy-move伪造检测方法。针对图像copy-move伪造检测,该方法优化设计了cGANs的损失函数,并使用适量的弱监督样本来提升网络性能。不同于目前大部分检测算法,该方法不仅可以定位出图像中的相似区域,还可以有效区分伪造来源区域和伪造目标区域。实验结果表明,本文所提出的方法在检测准确率上显著优于现有方法。      

An Efficient Image Forgery Detection Using Biorthogonal Wavelet Transform and Improved Relevance Vector Machine

Nowadays, the development of refined image processing and software editing tools has finish the exploitation of digital images easily and invisible the image to the normal eyes and this process known as image fakery. Image security is one of the key issues in any field that makes use of digital images. Copy-move forgery (CMF) is the most effective and simple scheme to create forged digital images. In general, the methodologies based on Scale Invariant Feature Transform (SIFT) are widely used to detect CMF. Unfortunately, the detection performance of all SIFT based CMF detection approaches are extremely dependent on the selection of feature vectors. The values of these parameters are often determined through experience or some experiments on a number of forgery images. However, these experience parameter values are not applicable to every image thereby offers a limited usefulness. This paper deals the CMF problem using improved Relevance Vector Machine technique. The key idea of the IVRM is to apply Biorthogonal Wavelet Transform based scheme on image for feature extraction. The feature vectors are then stored lexicographically and similarity of vectors is decided using Minkowski distance and threshold value. The simulation results of proposed technique show a significant improvement in accuracy, sensitivity, and specificity rates over others existing schemes.     

Detection and localization of forgery using statistics of DCT and Fourier components

In this paper, we present a comprehensive approach for investigating JPEG compressed test images, suspected of being tampered either by splicing or copy-move forgery (cmf). In JPEG compression, the image plane is divided into non-overlapping blocks of size 8 × 8 pixels. A unified approach based on block-processing of JPEG image is proposed to identify whether the image is authentic/forged and subsequently localize the tampered region in forged images. In the initial step, doubly stochastic model (dsm) of block-wise quantized discrete cosine transform (DCT) coefficients is exploited to segregate authentic and forged JPEG images from a standard dataset (CASIA). The scheme is capable of identifying both the types of forged images, spliced as well as copy-moved. Once the presence of tampering is detected, the next step is to localize the forged region according to the type of forgery. In case of spliced JPEG images, the tampered region is localized using block-wise correlation maps of dequantized DCT coefficients and its recompressed version at different quality factors. The scheme is able to identify the spliced region in images tampered by pasting uncompressed or JPEG image patch on a JPEG image or vice versa, with all possible combinations of quality factors. Alternatively, in the case of copy-move forgery, the duplication regions are identified using highly localized phase congruency features of each block. Experimental results are presented to consolidate the theoretical concept of the proposed technique and the performance is compared with the already existing state of art methods.     

基于SVD和直方图的JPEG图像篡改盲检测算法

现有的大多数图像篡改检测算法不能够很好地检测多次篡改区域,针对此不足,提出了一种有效的基于SVD和直方图的JPEG图像篡改盲检测算法。该算法首先以设定的窗口块在待检测图像上依次滑动一个像素得到每个单独的滑窗分块,每个分块用奇异值分解(SVD)值表征;然后字典排序所有分块量化后的SVD值矩阵,并通过统计排序后的矩阵的偏移频率来得到直方图;最后通过直方图设定阈值以判断分块是否属于复制粘贴块。实验结果表明,该算法不仅能对单次篡改区域进行准确定位,还能较好地检测到多次篡改区域。     

稳健性图像复制-黏贴篡改被动认证

图像复制-黏贴(copy-move)是一类的常见的图像篡改手段,篡改通过将图像中一部分区域复制并黏贴到同一幅图像另一区域后起到掩盖被覆盖内容的目的。由于篡改者为了使篡改更加逼真或者试图增加检测难度,往往在黏贴图像块之前对图像块进行加噪、模糊或者旋转缩放等后续处理。目前检测这类篡改的认证方法主要归纳为三类：变换域鲁棒特征子块匹配方法、旋转不变特征子块匹配方法和特征点匹配方法。本文对采用这三类方法的国内外文献进行了系统的分析和归纳并对未来研究方向进行了展望。     

Copy-move image forgery detection based on Gabor magnitude

With advancement of media editing software, even people who are not image processing experts can easily alter digital images. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. The most common method of tampering with a digital image is copy-move forgery, in which a part of an image is duplicated and used to substitute another part of the same image at a different location. In this paper, we present an efficient and robust method to detect such artifacts. First, the tampered image is segmented into overlapping fixed-size blocks, and the Gabor filter is applied to each block. Thus, the image of Gabor magnitude represents each block. Secondly, statistical features are extracted from the histogram of orientated Gabor magnitude (HOGM) of overlapping blocks, and reduced features are generated for similarity measurement. Finally, feature vectors are sorted lexicographically, and duplicated image blocks are identified by finding similarity block pairs after suitable post-processing. To enhance the algorithm’s robustness, a few parameters are proposed for removing the wrong similar blocks. Experiment results demonstrate the ability of the proposed method to detect multiple examples of copy-move forgery and precisely locate the duplicated regions, even when dealing with images distorted by slight rotation and scaling, JPEG compression, blurring, and brightness adjustment.     

一种基于逆序广义2近邻的图像多重复制粘贴篡改检测算法

为了解决数字图像多重复制粘贴篡改检测问题,克服广义2近邻(g2NN)算法对匹配特征点漏检的缺点,该文提出逆序广义2近邻(Rg2NN)算法。在计算匹配特征点时,该算法采用逆序方式计算特征点之间的匹配关系,可以更加准确地计算出所有与待检测特征点相匹配的特征点。实验证明,Rg2NN算法比g2NN算法计算出来的匹配特征点更加准确,提高了g2NN算法对多重复制粘贴篡改图像的检测能力,当图像中的一块区域被复制后在多处粘贴,或多块区域被复制粘贴时可以准确计算出复制粘贴区域。     

图像镜像复制粘贴篡改检测中的FI-SURF算法

针对数字图像版权中的复制粘贴篡改问题,提出FI-SURF (flip invariant SURF)算法。研究了当图像经过镜像翻转后SURF (speeded-up robust features)特征描述符的排列变化关系。提取SURF特征点后,将其特征描述符重新排序,即使复制粘贴区域经过镜像翻转,对应的特征点依然可以进行匹配。实验证明,FI-SURF算法在保留SURF算法运算速度快、顽健性强等优点的前提下,可有效检测出经过镜像翻转的复制粘贴区域,计算出复制粘贴区域的轮廓。     

Enhanced block-based copy-move forgery detection using k-means clustering

Multidimensional Systems and Signal Processing - The goal of copy-move forgery is to manipulate the semantics of an image. In fact, this can be performed by cloning a region of an image and...     

一种基于SIFT的仿射不变特征提取新方法

图像局部特征提取是图像理解及机器视觉领域一个非常关键的问题,其中SIFT特征因具有良好的显著性和鲁棒性而得到广泛应用。但是,SIFT采用DOG检测子,定位的特征区域为各向同尺度变化的圆形区域,故其只具有尺度不变性,并不具备仿射不变性。此外,SIFT采用128维特征向量表示,当在图像特征点较多情况下进行匹配实验时,存在存储空间大、匹配耗时多等缺点。针对这两个问题,本文提出一种新的仿射不变特征提取方法,即HA-DR-SIFT(Hessian Affine-Dimensionality Reduction-SIFT)。首先,用Hessian-Affine 检测子代替DOG检测子,使提取的椭圆图像区域满足仿射不变性需求;其次,用PCA或NLPCA方法对128维特征向量进行降维处理,提高后续运算效率。实验表明,新方法不仅具有良好的仿射不变性,而且在匹配时间和存储空间上优于SIFT算子。