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。     

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.     

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。     

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.     

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.     

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。     

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.     

基于Gabor小波与HOG特征的目标检测方法

针对传统的HOG目标识别方法,提出一种通过Gabor滤波融合后的进行HOG特征提取的目标检测方法。为了提高HOG特征提取信息的有效性,首先用Gabor对目标图像做了预处理,其预处理过程是针对图像Gabor特征的在尺度和方向上进行融合,形成一幅Gabor图像。为了有效提取全局的Gabor图像纹理、轮廓信息,将该图像分为大小相同且重叠的块,分别对每个块进行统计,最后用RealAdaboost级联方法对目标和非目标样本进行学习,并对测试序列进行分类。结果表明,基于梯度的Gabor预处理技术能提高目标特征提取性能。与传统的HOG目标识别的方法比较,该方法在目标图像受到干扰的情况(遮挡、重叠等)下,监测效果明显优越。     

A new Copy-Move forgery detection method using LIOP

The most prevalent type of digital image falsification occurs when a portion of a image is copied and pasted onto another section of the same image. Falsification of the image made in this way is called copy-move forgery (CMF). This study presents a new and effective approach for copy-move forgery detection (CMFD) using the Local Intensity Order Pattern (LIOP) to overcome the restrictions of existing CMFD techniques. The input image is first converted to a YCbCr color space and then split into Y, Cb, and Cr color channels. The LIOP features are then extracted from each color channel and all the features are combined. The feature vectors are ordered lexicographically and related features are detected by comparing the LIOP features. Although the LIOP feature has rarely been used in CMFD prior to this study, the success rate of the proposed method is high. In addition, since the channels are not correlated to each other in the YCbCr color space, each color channel is considered as a gray image, and the success rate is increased by combining the features extracted from each of the color channels. The proposed approach was assessed using the CoMoFoD and GRIP datasets. Experimental findings demonstrated that the suggested method was successful and displayed robustness in post-processing attacks.     

Active appearance models using statistical characteristics of Gabor based texture representation

Active appearance model (AAM) has been successfully applied to register many types of deformable objects in images. However, the high dimension of intensity used in AAM usually leads to an expensive storage and computational cost. Moreover, intensity values cannot provide enough information for image alignment. In this paper, we propose a new AAM method based on Gabor texture feature representation. Our contributions are two-fold. On one hand, based on the assumption that Gabor magnitude and Gabor phase follow a lognormal distribution and a general Gaussian distribution respectively, three simplified texture representations are proposed. One the other hand, we apply the proposed texture representations in AAM, which is the first time to extract statistical features from both Gabor magnitude and Gabor phase as the texture representation in AAM. Tests on public and our databases show that the proposed Gabor representations lead to more accurate and robust matching between model and images.     

A current-reuse biomedical amplifier with a NEF < 1

Digital image authenticity is always an imperative question to tackle whenever a digital image is being assessed for its content. Using digital forensic algorithms, the image will be evaluated for various traces left from numerous categories of manipulations including, among others, copy–move operations. Later this is considered an essential block in most digital image forgeries. It results in changing the information incorporated in a scene, hiding information from an image, or emphasizing some parts of the image. In this paper we propose and investigate two main approaches that differ in the feature extraction process in order to detect copy–move traces. In the first method, we use two-dimensional discrete cosine transform. Whereas in the second method, the phase response of Gabor filter is being used. Instead of being applied on the image directly, the two methods are applied over the first, the second or the third principal component of the image after being divided into overlapping blocks. Combining these conditions results in six basic implementations that are investigated under three parameters that must be optimized: block dimension, contrast and similarity thresholds. Results from testing and validation process demonstrate that the highest performance, in terms of false accept rate, is obtained when using Gabor filter associated with the first principal component of the image outperforming a reference method we implemented as well.     

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.     

Joint image splicing detection in DCT and Contourlet transform domain

Splicing is a fundamental and popular image forgery method and image splicing detection is urgently called for digital image forensics recently. In this paper, a Markov based approach is proposed to detect image splicing. The paper applies the Markov model in the block discrete cosine transform (DCT) domain and the Contourlet transform domain. First, the original Markov features of the inter-block between block DCT coefficients are improved by considering the different frequency ranges of each block DCT coefficients. Then, additional features are extracted in Contourlet transform domain to characterize the dependency of positions among Contourlet subband coefficients. And these features are extracted from single color channel for gray image while extracted from three color channels for color image. Finally, Support Vector Machines (SVMs) are exploited to classify the authentic and spliced images for the gray image dataset while ensemble classifier to the color image dataset. The experiment results demonstrate that the proposed detection scheme outperforms some state-of-the-art methods when applied to Columbia Image Splicing Detection Evaluation Dataset (DVMM), and ranks fourth in phase 1 on the Live Ranking of the first Image Forensics Challenge.     

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。      

基于Gabor和PCA的面部图像识别方法

文中利用Gabor变换和PCA降维的优点,提出了Gabor+PCA的面部图像识别方法.该方法先提取图像的Gabor特征,然后将Gabor特征与原图像特征结合构成新的融合特征并用PCA降维,最后用KNN分类器分类.所提Gabor+PCA方法不仅能挖掘出图像的细节信息,而且拓宽了特征空间的维数.另外,Gabor+PCA方法...     

方向无关遥感影像的纹理分类算法

首先根据遥感影像空间分辨率较低,局部区域内图像纹理变化不大的特点,对遥感影像进行合理的分块,使得各分块具有单一的主纹理特征;然后利用Gabor小波变换提取各图像块的纹理特征向量,并通过简单的循环移位算法,方便的形成方向无关的纹理特征向量;进而引入改进的棋盘距离来描述图像块间的相似度,最后使用无监督聚类算法对影像块进行分类,达到了很好的分类效果．     

Image quality assessment based on adaptive multiple Skyline query

Non-reference image quality assessment has attracted great emphasis in recent years. Traditional image quality assessment algorithms based on structural similarity cannot make full use of the image gradient features, and the contrast similarity features often ignore the consistency of continuous color blocks within the image, which leads to large discrepancy between the evaluation result and the subjective judgment of human vision system. In this paper, we propose a deep model for image quality assessment where the spatial and visual features of image are both considered. For better feature fusion, we design an adaptive multiple Skyline query algorithm named MSFF, which takes as input multiple features of images, and learns the feature weights through end-to-end training. Extensive experiments on image quality assessment tasks prove that the proposed model exhibits superior performance compared with existing solutions.