Copy-move Forgery Detection Method Research Articles

Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators

ABSTRACTThis paper presents a new method for copy-move forgery detection of duplicated objects. A bounding rectangle is drawn around the detected object to form a sub-image. Morphological operator is used to remove the unnecessary small objects. Highly accurate polar complex exponential transform moments are used as features for the detected objects. Euclidian distance and correlation coefficient between the feature vectors are calculated and used for searching the similar objects. A set of 20 forged images with duplicated objects is carefully selected from previously published works. Additional 80 non-forged images are edited by the authors and forged by duplicating different kinds of objects. Numerical simulation is performed where the results show that the proposed method successfully detect different kinds of duplicated objects. The proposed method is much faster than the previously existing methods. Also, it exhibits high robustness to various attacks such as additive white Gaussian noise, JPEG compression, rotation, and scaling.

Copy-move forgery detection based on convolutional kernel network

Conventional copy-move forgery detection methods mostly make use of hand-crafted features to conduct feature extraction and patch matching. However, the discriminative capability and the invariance to particular transformations of hand-crafted features are not good enough, which imposes restrictions on the performance of copy-move forgery detection. To solve this problem, we propose to utilize Convolutional Kernel Network to conduct copy-move forgery detection. Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional architecture. It can achieve competitive performance for its excellent discriminative capability. To well adapt to the condition of copy-move forgery detection, three significant improvements are made: First of all, our Convolutional Kernel Network is reconstructed for GPU. The GPU-based reconstruction results in high efficiency and makes it possible to apply to thousands of patches matching in copy-move forgery detection. Second, a segmentation-based keypoint distribution strategy is proposed to generate homogeneous distributed keypoints. Last but not least, an adaptive oversegmentation method is adopted. Experiments on the publicly available datasets are conducted to testify the state-of-the-art performance of the proposed method.

An improved block-based matching algorithm of copy-move forgery detection

Copy-move forgery is a common way of image tampering. Matching algorithm is the key step in copy-move forgery detection. Usually, the classical block-based matching algorithm (CBMA) can’t find all matched sub-blocks. In this paper, we propose an improved block-based matching algorithm (IBMA) to solve the problem. Firstly, we put the sum of feature vectors in the first column to get a new matrix. Secondly, the matrix is sorted by first column. Finally, every row of the matrix will search the following rows until the difference in the first column is larger than the threshold value. Experiment results show that the improved block-based matching algorithm is better than the classical block-based matching algorithm when an image was distorted by Gaussian noise, salt-pepper noise, or JPEG compression. The reason is that improved block-based matching algorithm can look for all matched sub-blocks, which makes copy-move forgery detection methods more robust.

Fast reflective offset-guided searching method for copy-move forgery detection

In recent years, the detection of copy-move forgery has become an important research topic in multimedia forensics and security. However, the computational complexity of the existing methods is still very high. This paper proposes a novel and fast reflective offset-guided searching method for image copy-move forgery detection. During the initialization stage, the features are extracted and randomly assigned feature correspondences to obtain the initial mapping offsets. In the searching stage, the reflective offsets are computed to estimate whether the mapping offsets are copy-move forgery mapping offsets. Then, the proposed priority based feature matching to rapidly propagate the copy-move forgery mapping offsets and optimize the mapping and reflective offsets. Finally, only a few iterations can detect the forgery regions completely from the mapping offsets. Experimental results show that the proposed method for image copy-move forgery detection greatly reduces the computational complexity and achieves better detection results compared with existing state-of-the-art copy-move forgery detection algorithms, even under various challenging conditions.

SIFT-Symmetry: A robust detection method for copy-move forgery with reflection attack

Copy-move forgery (CMF) is a popular image manipulation technique that is simple and effective in creating forged illustrations. The bulk of CMF detection methods concentrate on common geometrical transformation attacks (e.g., rotation and scale) and post-processing attacks (e.g., Joint Photographic Experts Group (JPEG) compression and Gaussian noise addition). However, geometrical transformation that involves reflection attacks has not yet been highlighted in the literature. As the threats of reflection attack are inevitable, there is an urgent need to study CMF detection methods that are robust against this type of attack. In this study, we investigated common geometrical transformation attacks and reflection-based attacks. Also, we suggested a robust CMF detection method, called SIFT-Symmetry, that innovatively combines the Scale Invariant Feature Transform (SIFT)-based CMF detection method with symmetry-based matching. We evaluated the SIFT-Symmetry with three established methods that are based on SIFT, multi-scale analysis, and patch matching using two new datasets that cover simple transformation and reflection-based attacks. The results show that the F-score of the SIFT-Symmetry method surpassed the average 80% value for all geometrical transformation cases, including simple transformation and reflection-based attacks, except for the reflection with rotation case which had an average F-score of 65.3%. The results therefore show that the SIFT-Symmetry method gives better performance compared to the other existing methods.

A copy-move forgery detection method based on CMFD-SIFT

A very common way of image tampering is the copy-move attack. When creating a copy-move forgery, it is often necessary to add or remove important objects from an image. To carry out forensic analysis of such images, various copy-move forgery detection (CMFD) methods have been developed in the literatures. In recent years, many feature-based CMFD approaches have emerged due to its excellent robustness to various transformations. However there is still place to improve performance further. Many of them would suffer from the problem of insufficient matched key-points while performing on the mirror transformed forgeries. Furthermore, many feature-based methods might hardly expose the tempering when the forged region is of uniform texture. In this paper, a novel feature-based CMFD method is proposed. Key-points are detected by using a modified SIFT-based detector. A novel key-points distribution strategy is developed for interspersing the key-points evenly throughout an image. Finally, key-points are descripted by an improved SIFT descriptor which is enhanced for the CMFD scenario. Extensive experimental results are presented to confirm the efficacy.

Copy-move forgery detection based on hybrid features

Copy-move forgery is one of the most popular tampering artifacts in digital images. Most existing keypoint-based detection methods may fail to precisely locate duplicated regions because there are not enough correct matched points. In this paper, a novel copy-move forgery detection method is proposed based on hybrid features. A robust interest point detector KAZE is introduced and combined with SIFT to extract more feature points. In order to deal with multiple duplications, an improved matching algorithm is used which can find the n-best matched features. Then an effective filtering step based on image segmentation is executed to filter out false matches. Moreover, an iteration strategy is developed to estimate transformation matrices and determine the existence of forgery. Based on these matrices, the duplicated regions can be located at pixel level. Experimental results demonstrated that the proposed method can precisely detect duplicated regions even after distortions such as rotation, scaling, JPEG compression and adding noise.

A new block-based method for copy move forgery detection under image geometric transforms

Copy move forgery detection (CMFD) is one of the most active subtopic in forgery scheme. The methods of CMFD are divided into to block-based method and keypoint-based method in general. Compared with keypoint-based method, block-based method can detect undetectable detail without morphology segmentation. But many block-based methods detect the plain copy-move forgeries only. They have been incompetent to detect the post-processing operations such as various geometrical distortions, and then fail to detect the forgery regions accurately. Therefore, this paper presents an improved block-based efficient method for CMFD. Firstly, after pre-processing, an auxiliary overlapped circular block is presented to divide the forged image into overlapped circular blocks. The local and inner image feature is extracted by the Discrete Radial Harmonic Fourier Moments (DRHFMs) with the overlapped circular block from the suspicious image. Then, the similar feature vectors of blocks are searched by 2 Nearest Neighbors (2NN) test. Euclidean distance and correlation coefficient is employed to filter these features and then remove the false matches. Morphologic operation is employed to delete the isolated pixels. A series of experiments are done to analyze the performance for CMFD. Experimental results show that the new DRHFMs can obtain outstanding performance even under image geometrical distortions.

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.

RST Invariant Image Forgery Detection

Background/Objectives: Copy-Move Forgery Detection (CMFD) is a very prevalent approach used to detect copy and pasted portions of the same image. The copied portion is rotated, flipped or scaled. The detection method should be invariant to rotation, scaling and translation. Many CMFD methods came into existence; however, some methods fail to withstand attacks such as Contrast adjustment, Gaussian blur and JPEG Compression. Although the methods are able to resist the attacks, they are computationally complex. This paper proposes a Rotation, Scaling, Translation (RST) invariant image forgery detection. Methods: Local Binary Pattern (LBP) is applied on the low frequency content of Discrete Wavelet Transform (DWT) decomposed image for feature extraction. Findings: The proposed method is invariant to rotation, scaling and translation attacks on the pasted portions of the image and able to resist post-processing attacks and has low computational effort. It is evaluated qualitatively and quantitatively on a CASIA database. Morphological operations are performed to reduce the false alarms. The correct detection ratio is in the range of 80% to 99% and false detection ratio in the range of 7% to 30%. Applications: There is a great demand to detect the forgery, which aids in the digital forensic analysis, in legal document substantiation, and various other fields.

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.

Keypoints-Based Image Passive Forensics Method for Copy-Move Attacks

A novel image passive forensics method for copy-move forgery detection is proposed. The proposed method combines block matching technology and feature point matching technology, and breaks away from the general framework of the visual feature-based approach that used local visual feature such as SIFT and followed by a clustering procedure to group feature points that are spatially close. In our work, image keypoints are extracted using Harris detector, and the statistical features of keypoint neighborhoods are used to generate forensics features. Then we proposed a new forensics features matching approach, in which, a region growth technology and a mismatch checking approach are developed to reduce mismatched keypoints and improve detected accuracy. We also develop a duplicate region detection method based on the distance frequency of corresponding keypoint pairs. The proposed method can detect duplicate regions for high resolution images. It has higher detection accuracy and computation efficiency. Experimental results show that the proposed method is robust for content-preserving manipulations such as JPEG compression, gamma adjustment, filtering, luminance enhancement, blurring, etc.

Survey on Keypoint Based Copy-move Forgery Detection Methods on Image

One of the problem in image forensics is to check the authenticity of image. This can be very important task when images are used as an evidence which cause change in judgment like, for example in a court of law. Image is forged by using different techniques but in that most common technique is copy-move forgery. Copy-move forgery is created by copying the region from a particular image and pasting that region on same image to mislead the user. This type of forgery is done using availability of new sophisticated software and applications. This type of forgery is also done in video. In this paper we survey on different keypoint based copy-move forgery detection methods with different parameters.

Exposing region duplication through local geometrical color invariant features

Many advanced image-processing softwares are available for tampering images. How to determine the authenticity of an image has become an urgent problem. Copy–move is one of the most common image forgery operations. Many methods have been proposed for copy–move forgery detection (CMFD). However, most of these methods are designed for grayscale images without any color information used. They are usually not suitable when the duplicated regions have little structure or have undergone various transforms. We propose a CMFD method using local geometrical color invariant features to detect duplicated regions. The method starts by calculating the color gradient of the inspected image. Then, we directly take the color gradient as the input for scale invariant features transform (SIFT) to extract color-SIFT descriptors. Finally, keypoints are matched and clustered before their geometrical relationship is estimated to expose the duplicated regions. We evaluate the detection performance and computational complexity of the proposed method together with several popular CMFD methods on a public database. Experimental results demonstrate the efficacy of the proposed method in detecting duplicated regions with various transforms and poor structure.

Copy-move Image Forgery Detection Using an Efficient and Robust Method Combining Un-decimated Wavelet Transform and Scale Invariant Feature Transform

In the present digital world, digital images and videos are the main carrier of information. However, these sources of information can be easily tampered by using readily available software thus making authenticity and integrity of the digital images an important issue of concern. And in most of the cases copy- move image forgery is used to tamper the digital images. Therefore, as a solution to the aforementioned problem we are going to propose a unique method for copy-move forgery detection which can sustained various pre-processing attacks using a combination of Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT). In this process first DyWT is applied on a given image to decompose it into four parts LL, LH, HL, and HH. Since LL part contains most of the information, we intended to apply SIFT on LL part only to extract the key features and find a descriptor vector of these key features and then find similarities between various descriptors vector to conclude that there has been some copy-move tampering done to the given image. And by using DyWT with SIFT we are able to extract more numbers of key points that are matched and thus able to detect copy-move forgery more efficiently.

Copy-move forgery detection using multiresolution local binary patterns

Copy-move forgery is one of the most popular tampering artifacts in digital images. In this paper, we present an efficient method for copy-move forgery detection using Multiresolution Local Binary Patterns (MLBP). The proposed method is robust to geometric distortions and illumination variations of duplicated regions. Furthermore, the proposed block-based method recovers parameters of the geometric transformations. First, the image is divided into overlapping blocks and feature vectors for each block are extracted using LBP operators. The feature vectors are sorted based on lexicographical order. Duplicated image blocks are determined in the block matching step using k-d tree for more time reduction. Finally, in order to both determine the parameters of geometric transformations and remove the possible false matches, RANSAC (RANdom SAmple Consensus) algorithm is used. Experimental results show that the proposed approach is able to precisely detect duplicated regions even after distortions such as rotation, scaling, JPEG compression, blurring and noise adding.

Automated Forensic Method for CopyMove Forgery Detection based on Harris Interest Points and SIFT Descriptors

Automated Forensic Method for CopyMove Forgery Detection based on Harris Interest Points and SIFT Descriptors