Copy-move Forgery Detection Technique Research Articles

S-transform-based efficient copy-move forgery detection technique in digital images

S-transform-based efficient copy-move forgery detection technique in digital images

An Improved Discrete Cosine Transformation Block Based Scheme for Copy-move Image Forgery Detection

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF.

Grey wolf assisted SIFT for improving copy move image forgery detection

Copy-move forgery is a general widespread type of digital image forgery, where a segment of an image is attached into a new portion of the similar image to hide or replicate the parts which are called forgered image. The forgered image appears original, as the objective region in spite of being forged, has attained the fundamental qualities of the similar image itself. The capability of the copy-move forgery detection (CMFD) technique is lacked due to some post-processing functions, like JPEG compression scaling, or rotation, etc. Therefore, this paper intends to develop a CMFD using Scale-invariant feature transform (SIFT), best-fin-first algorithm (BBF) and RANdom SAmple Consensus (RANSAC) directed by grey wolf optimization (GWO) algorithm. Initially, the keypoints are selected using SIFT principle, and BBF algorithm identifies the matched keypoints using keypoint threshold. Further, SIFT feature descriptor is determined, and the final extracted paired keypoints are given to RANSAC algorithm to remove all the mismatched keypoints. In this CMFD model, the parameters such as parameters keypoint threshold, maximum distance of inliers in RANSAC and distance threshold in SIFT features are optimized using GWO. The foremost purpose of this research work is maximizing the number of paired keypoints. Hence the proposed model is termed as GWO-based parameter optimization for CMFD (GWPO-CMD). The proposed model is compared over several other meta-heuristic-based keypoint threshold selections and proves its efficiency through diverse analysis.

Detection of copy-move forgery using AKAZE and SIFT keypoint extraction

Digital image manipulation techniques are becoming increasingly sophisticated and widespread. Copy-move forgery is one of the frequently used manipulation techniques. In this paper, we propose a keypoint based copy-move forgery detection (CMFD) technique, which is a combination of accelerated KAZE (AKAZE) and scale invariant feature transform (SIFT) features. By using AKZAE and SIFT, a significant number of keypoints are extracted even in a smooth region to detect the manipulated regions efficiently. After formation of the mixed keypoints, the g2NN is used for matching process to locate the duplicated regions. The experimental results show that the proposed method can detect the duplicated regions even if the image is post-processed with scaling, rotation, noise and JPEG compression operations. To validate the robustness and effectiveness of the proposed method, a statistical analysis is performed using the ANOVA method.

Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

This paper presents the state-of-the-art technical reviews and analysis of recent copy-move forgery detection (CMFD) techniques. A new CMFD process pipeline was introduced. In addition, the techniques used in each stage of the CMFD pipeline are summarized and classified into small categories. Furthermore, the tables of comparison are provided as a quick reference. This technical review paper is expected to help provide useful insights and updated information regarding recent advancements in CMFD to researchers in the field.

A Robust Passive Blind Copy-Move Image Forgery Detection

Image forensic is the important research area which deals to verify the authenticity of the digital image. Copy-move forgery is common type of forgery used to modify the image. This paper proposes passive blind forensic copy-move forgery detection technique. The combination of dyadic wavelet transform (DyWT) and scale invariant feature transform (SIFT) is used. Finally, RANSAC is used to improve the detection accuracy. The performance of the algorithm is verified with 45 original/forged images considering various attacks, such as scaling, rotation, small object and multiple copy-move pasting. The experimental results reveal that the proposed technique identifies and locates the forged area accurately for all attacks. The comparison of the proposed method is done with existing methods in terms of detection accuracy, recall, and precision. The simulation results show that for the most of the cases the proposed method outperforms the existing methods.

An Improved DCT Block Based Technique for Copy-Move Forgery Detection in Medical Images

Copy-Moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The most commonly used technique is the block based which divides image into blocks during the stage of image pre-processing before features are extracted whereas key point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. The proposed technique is based on DCT and an improvement on DCT technique is achieved in terms of dimensionality reduction using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy. Based on the analysis of this work as compared to previous proposed works, since previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the octagonal block was observed as the improvement over the previously proposed dimensionality reduction using the circle block.

CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

With the advancement of image editing tools in today's world, the manipulation of images like cropping, cloning, resizing, etc., becomes an easy proposition and on the other end, checking or determining whether an image has been manipulated or not, becomes a great challenge. Copy-move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The detection of copy-move forgery has become a prominent research area. This study presents a detailed review and critical discussions with pros and cons of each of copy-move forgery detection techniques from 2007 to 2017. This study also addresses the variation in databases, issues, challenges, future directions and references in this domain.

Review, analysis and parameterisation of techniques for copy–move forgery detection in digital images

Copy-move forgery is one of the most preliminary and prevalent forms of modification attack on digital images. In this form of forgery, region(s) of an image is(are) copied and pasted onto itself, and subsequently the forged image is processed appropriately to hide the effects of forgery. State-of-the-art copy-move forgery detection techniques for digital images are primarily motivated toward finding duplicate regions in an image. The last decade has seen lot of research advancement in the area of digital image forensics, whereby the investigation for possible forgeries is solely based on post-processing of images. In this study, the authors present a three-way classification of state-of-the-art digital forensic techniques, along with a complete survey of their operating principles. In addition, they analyse the schemes and evaluate and compare their performances in terms of a proposed set of parameters, which may be used as a standard benchmark for evaluating the efficiency of any general copy-move forgery detection technique for digital images. The comparison results provided by them would help a user to select the most optimal forgery detection technique, depending on the author requirements.

Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images

The most common image tampering often for malicious purposes is to copy a region of the same image and paste to hide some other region. As both regions usually have same texture properties, therefore, this artifact is invisible for the viewers, and credibility of the image becomes questionable in proof centered applications. Hence, means are required to validate the integrity of the image and identify the tampered regions. Therefore, this study presents an efficient way of copy-move forgery detection (CMFD) through local binary pattern variance (LBPV) over the low approximation components of the stationary wavelets. CMFD technique presented in this paper is applied over the circular regions to address the possible post processing operations in a better way. The proposed technique is evaluated on CoMoFoD and Kodak lossless true color image (KLTCI) datasets in the presence of translation, flipping, blurring, rotation, scaling, color reduction, brightness change and multiple forged regions in an image. The evaluation reveals the prominence of the proposed technique compared to state of the arts. Consequently, the proposed technique can reliably be applied to detect the modified regions and the benefits can be obtained in journalism, law enforcement, judiciary, and other proof critical domains.

A Robust and Efficient Copy-Move Forgery Detection Technique based on SIFT and SVD

Objective: To detect copy-move forgery from a given digital image by reducing false detection thus increasing precision rate and F1 score. It is also invariant to scaling, rotation, noise attack. Methods/Analysis: The paper describes an effective and novel method to detect the copy-move forgery detection using Singular Value Decomposition (SVD) and Scale-Invariant Feature Transform (SIFT) features. It divides the given image into equal sized blocks and applies keypoint detection and feature descriptor for each block of the image. Then again SVD is calculated from128 SIFT descriptor to detect the forgery part of the given image. In this approach, a correlation is calculated for different images under different attacks like scaling, rotation, and noise. Finding: The proposed system is tested using standard image data against various types of image attacks like scaling, rotation, noise, blur etc. From the result, it is found that the proposed system is robust and invariant to most of the image attacks. The proposed system easily detects the copied part of the image more efficiently compared to other block based method since SIFT feature is invariant to scaling, rotation, noise, blur etc. Novelty/Improvement: The proposed methods uses block based technique and apply SIFT and SVD technique for detecting forged region in the given image. False detection is reduced in the proposed system which increases the precision and F1 score.

Key-Point Based Copy-Move Forgery Detection and Their Hybrid Methods: A Review

Copy-move image forgery is one of the tampering techniques that need to be tackled with. Many copy-move forgery detection techniques such as exhaustive search, block and key-point matching based methods have been proposed for the detection of copy-move image forgery. Although key-point based methods were found better than block based methods in terms of computationalefficiency, space complexity and robustness against rotation and scaling. However, key-point based methods also possess a number of limitations. So, researchers have proposed many integrated methods to cope up with the limitations of key-point based methods and to make copy move forgery detection more reliable. In this paper, keypoint based methods such as SIFT, SURF, ORB and theirintegrated methods are reviewed.

Recent Block-based Methods of Copy-Move Forgery Detection in Digital Images

Photographs are taken as valid evidences in various scenarios of our day to day life. Because of the developments in the field of Image Processing, altering images according to ones need is not a difficult task. Techniques of Image Forensics play its crucial role at this juncture. One of the mostly found types of image tampering is Copy-Move forgery. A copymove forgery is performed by copying a region in an image and pasting it on another region in the same image, mostly after some form of post-processing like rotation, scaling, blurring, noise addition, JPEG compression etc. Two types of copy-move forgery detection techniques exist in literature. They are the Block based methods and Key-point based methods. Both the methods have their own advantages and limitations. This paper presents a survey on the recent developments in block based methods.

A survey of partition-based techniques for copy-move forgery detection.

A copy-move forged image results from a specific type of image tampering procedure carried out by copying a part of an image and pasting it on one or more parts of the same image generally to maliciously hide unwanted objects/regions or clone an object. Therefore, detecting such forgeries mainly consists in devising ways of exposing identical or relatively similar areas in images. This survey attempts to cover existing partition-based copy-move forgery detection techniques.