Region Duplication Forgery Research Articles

Investigation of image forgery based on multiscale retinex under illumination variations

The number of forged images is currently expanding vastly over the Internet. Therefore, image authenticity represents a globally challenging issue that must be addressed. Emerging tools for image editing have been developed to manipulate and enhance digital images; however, forgers can exploit these tools to achieve their destructive purposes. Forgers often use a common method of image forgery called region duplication forgery. In this method, the copied region in the fake image can appear identical to the original region of the image. This paper aims to target this issue by developing an algorithm that can detect suspected images through localizing small duplicated regions. These regions can be described by multiscale features, which are invariant with illumination variations. The proposed method begins with segmenting suspected images using an adaptive statistical region merging. The goal of the segmentation method is to discover small regions. The method then targets the small regions based on color correction to represent their illumination features. Experiments are also conducted to validate the proposed method on two image datasets, Media Integration and Communication Center (MICC) and Image Data Manipulation, yielding positive results. A comparative study of the most recent methods is carried out.

Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation

In this paper, we present a passive blind scheme consisting of two different algorithms to detect frame and region duplication forgeries in videos. We have examined the video frame duplication forgery in three different forms such as duplication of a sequence of consecutive video frames at long continue running position, duplication of many such sequences having different lengths at many different locations and duplication from other videos having different and same dimensions which can raise a serious problem in the real world scenario. The algorithm I of proposed scheme has detected these three different forms of copy-moved frame duplication forgery in videos by obtaining the mean features of each video frame for evaluating the correlation between sequences. In this paper, we have also analysed forged regular and irregular region within same frame at different locations and from other frame to one or more sequences of consecutive frames of the same video at same locations. It creates a challenge to detect this copy-move forgery due to slightly change in pixel intensity values in the duplicated region and providing high correlation as authentic region. The algorithm II of proposed scheme has detected these copy-moved region duplication forgeries in videos by locating the position of error with threshold process in order to calculate the similarities between regions of two frames or within affected frame. In this paper, the experimental results show the higher detection accuracy and execution time efficiency of proposed scheme than the latest algorithms with satisfactory performance.

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

In digital image forensics, local interest points can be employed to faithfully detect region duplication forgery. Authentic images may be abused by copy-move forgery to fully contained duplicated regions such as objects. Recent existing local interest point forgery detection methods fail to detect this type of forgery in the retouched regions by some geometric transformations. To solve this challenge, local interest points should be detected which cover all the regions with high primitives like corners and edges. These primitives represent the internal structure of any object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be exposed based on Scale-Invariant Features Transform (SIFT) algorithm. Here, we provide an image forgery detection technique by using local interest points. First, the image is segmented based on fuzzy C means to divide the image into homogenous regions that have the same texture. Second, local interest points are exposed by extracting Adaptive non-maximal suppression (ANMS) from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS Keypoints can be effectively utilized to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase Quantization (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v. 2 Image Datasets

An efficient forensic technique for exposing region duplication forgery in digital images

The internet users share a massive amount of digital images daily. The accessibility of powerful image manipulation tools has made the integrity of image contents questionable. The most popular image tampering is to duplicate a region elsewhere in the same image to replicate or conceal some other region. The duplicated regions have identical color and texture attributes that make this artifact invisible to the human eye. Therefore, efficient techniques are required to verify the credibility of image contents by detecting the regions duplicated in the digital images. This paper proposes an efficient technique for exposing region duplication forgery in digital images. The proposed technique divides the approximation (LL) sub-band of shift invariant stationary wavelet transform into overlapping blocks of w × w (i.e. w = 4, 8) sizes. The distinctive features extracted from the overlapping blocks are utilized to expose the region duplication forgeries in digital images. The experimental results of the proposed technique are compared with state-of-the-art techniques that reveal the prominence, and effectiveness of the proposed technique in terms of precision, recall and F1 score for different block sizes. Therefore, the proposed technique can reliably be applied to identify the counterfeited regions and the benefits of the proposed technique can be achieved in different fields for example crime investigation, news reporting, and judiciary.

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods.

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Due to the availability of powerful image editing softwares, forgers can tamper the image content easily. There are various types of image forgery, such as image splicing and region duplication forgery. Region duplication is one of the most common manipulations used for tampering digital images. It is vital in image forensics to authenticate the digital image. In this paper, a novel region duplication forgery detection approach is proposed. By segmenting the input image based on the colour features, sufficient number of centroids are produced, that exist even in small or smooth regions. Then, the Least Significant Bit (LSB) of all the colours of pixels in each segment are extracted to build the signature vector. Finally, the hamming distance is calculated through exploiting the signature vector of image to find the dissimilarity. Various experimental results are provided to demonstrate the superior performance of the proposed scheme under some post processing operations such as scaling attack.

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center– of the University of Florence) for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression.

Blind Detection of Region Duplication Forgery Using Fractal Coding and Feature Matching.

Digital image forgery detection is important because of its wide use in applications such as medical diagnosis, legal investigations, and entertainment. Copy-move forgery is one of the famous techniques, which is used in region duplication. Many of the existing copy-move detection algorithms cannot effectively blind detect duplicated regions that are made by powerful image manipulation software like Photoshop. In this study, a new method is proposed for blind detecting manipulations in digital images based on modified fractal coding and feature vector matching. The proposed method not only detects typical copy-move forgery, but also finds multiple copied forgery regions for images that are subjected to rotation, scaling, reflection, and a mixture of these postprocessing operations. The proposed method is robust against tampered images undergoing attacks such as Gaussian blurring, contrast scaling, and brightness adjustment. The experimental results demonstrated the validity and efficiency of the method.

Detection of Region-duplication Forgery in Image Based on Key Points' Binary Descriptors

As a simple and efiective way of image tampering, copy-move forgery was often used as a preferred method to forgers. A new method for detecting and localizing this type of malicious tampering is presented in this paper. The method flrst extracts Harris corners as the key points of the image, then extracts the binary feature descriptors of these key points, and searches the most similarity feature descriptor which satisfy a certain condition according to the descriptors, flnally localizes the tampered regions. The experiment results show that the method can efiectively detect this type of tampering for images. Simultaneously, the matching time is faster than these methods which descriptors’ storage type is ∞oating-point, such as SIFT and SURF.

Region Duplication Forgery Detection using Hybrid Wavelet Transforms

With the help of various image editing tools available, it has become easier to alter an image in such a way that it does not leave behind any clues. Copy –Move forgery or also known as Region Duplication Forgery is a type of image forgery in which a part of digital image is copied and pasted to another part of same image. Since the copied and pasted image comes from the same image, it becomes difficult to detect the forgery. Generally the intention behind Copy-Move forgery is to hide important objects in an image. In this paper, a hybrid wavelet transform based forgery detection method is proposed. Hybrid Wavelet transforms is generated from basic orthogonal transforms. Hybrid wavelet transforms combines other orthogonal transforms such as hybrid of Discrete Cosine Transform (DCT), Walsh Transform. Both DCT and Walsh transforms are orthogonal. They can be used to generate Hybrid Wavelet Transform. This paper proposes generating a Hybrid Wavelet Transform (DCT-Walsh Hybrid Wavelet Transform) from DCT and Walsh transforms and used to detect copy-move forgery. The image is divided into overlapping blocks. On each block, DCT-Walsh Hybrid wavelet transform is applied. From each block discriminative features are extracted from coefficients. These feature vectors are lexicographically sorted and block matching step is applied to find duplicated blocks.

Copy-Move Forgery Detection using Orthogonal Wavelet Transforms

With the help of various image editing tools available, it has become easier to alter an image in such a way that it does not leave behind any clues. Copy –Move forgery is a type of image forgery in which a part of digital image is copied and pasted to another part of same image. Since the copied and pasted image comes from the same image, it becomes difficult to detect the forgery. Generally the intention behind CopyMove forgery is to hide important objects in an image. In this paper, an orthogonal wavelet transform based forgery detection method is proposed. Orthogonal wavelet transform is generated from basic orthogonal transforms. We consider generating Discrete Cosine Transform Wavelet (DCTW) transform and Walsh Wavelet (WW) transform from DCT and Walsh orthogonal transforms. The image is divided into overlapping blocks. On each block, DCTW and WW transforms are applied. From each block discriminative features are extracted from coefficients. These feature vectors are lexicographically sorted and block matching step is applied to find duplicated blocks. General Terms Image Forgery, Digital Forensics, Region Duplication Forgery

Detection and location of image region duplication forgery based on phase correlation

Most existing algorithms for detecting region duplication forgery are of low efficiency.Concerning this problem,this paper proposed a detecting and locating algorithm based on phase correlation.Firstly,the image was divided into small blocks by the means of sliding window.It skipped multi-pixels every time.Those blocks that had more matching possibility were chosen through sorting grayscale mean of block.Secondly,whether two blocks were matched was decided and their relative translation was calculated using the impulse function peak of the cross-power spectrum.Lastly,the mismatched blocks got removed according to the number of matched blocks that had same offset distance,and the forgery regions were marked according to the matched blocks.The experimental results show that the proposed algorithm is efficient and robust to such operations as lossy compression,blur filter and noise.

Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

Region Duplication Forgery Detection Technique Based on SURF and HAC

Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

Image region duplication detection based on circular window expansion and phase correlation

Region duplication forgery is one of the tampering techniques that are frequently used, where a part of an image is copied and pasted into another part of the same image. In this paper, a phase correlation method based on polar expansion and adaptive band limitation is proposed for region duplication forgery detection. Our method starts by calculating the Fourier transform of the polar expansion on overlapping windows pair, and then an adaptive band limitation procedure is implemented to obtain a correlation matrix in which the peak is effectively enhanced. After estimating the rotation angle of the forgery region, a searching algorithm in the sense of seed filling is executed to display the whole duplicated region. Experimental results show that the proposed approach can detect duplicated region with high accuracy and robustness to rotation, illumination adjustment, blur and JPEG compression while rotation angle is estimated precisely for further calculation.

Detection of image region-duplication forgery based on gray level co-occurrence matrix

With regard to the copy-move forgery of image region,this paper proposes a detection algorithm based on gray level co-occurrence matrix.Firstly,we divided the detected image into multiple overlapping blocks with same sizes,represented the textural features of each block with the statistics of its gray level co-occurrence matrix,and got the feature vector of the image.Secondly,we sorted the feature vector by dictionary,and located the tampered region by utilizing the displacement vectors of image blocks.Lastly,experimental results show that our algorithm performs better than the classical detection algorithm based on Principal Component Analysis(PCA) in terms of the processing against rotate operation and of efficiency.

Digital image forgery detection and estimation by exploring basic image manipulations

In this modern age in which we are living, digital images play a vital role in many application areas. But at the same time the image retouching techniques has also increased which forms a serious threat to the security of digital images. To cope with this problem, the field of digital forensics and investigation has emerged and provided some trust in digital images. In this paper we proposed a technique for image authentication that detects the manipulations that are done in the digital images. In most of the image forgeries such as copy-and-paste forgery, region duplication forgery, image splicing forgery etc basic image operations or manipulations are often involved. Thus if there exists the evidence for basic image alterations in digital images we can say that the image has been altered. This paper aims at detecting the basic image operations such as re-sampling (rotation, rescaling), contrast enhancement and histogram equalization which are often done in forged images. The available interpolation related spectral signature method is used for detecting rotation and rescaling and for estimating parameters such as rotation angle and rescale factors. This rotation/rescaling detection method detects some unaltered images as altered one when the images are JPEG compressed. We have overcome that problem by adding noise in the input images. We have also used the existing fingerprint detection technique for detecting contrast enhancement and histogram equalization. Besides the techniques discussed in the existing method, we identified a unique property for histogram equalization which can help us to differentiate contrast enhancement from histogram equalization. This work is tested in USC-SIPI database which consists of general unaltered images and achieved results with satisfactory accuracy.

A passive image authentication scheme for detecting region-duplication forgery with rotation

Region-duplication forgery is one of most common tampering artifices. Several methods have been developed to detect and locate the tampered region, while most methods do fail when the copied region is rotated before being pasted because of the de-synchronization in the searching procedure. To solve the problem, the paper proposes an efficient and robust passive authentication method that uses the circle block and the Hu moments to detect and locate the duplicate regions with rotation. Experimental results show that our method is robust not only to noise contamination, blurring and JPEG compression, but also to the rotation. Meanwhile, the proposed method has better time performance compared with exiting methods because of the lower feature dimension.