A fast and high accurate image copy-move forgery detection approach

Copy-Move forgery is the most widely used way to tamper image for creating the forged images. The meaning of the photograph is changed by forging or concealing the images. Copy move is a very common technique for image forgery; tools used for retouching are applied along with it making naked eyes difficult to detect the copy move. Hence, the verification of authenticity and integrity is very essential of the image. The copy-move forgery detection can be divided into two heads a) Block based method b) Keypoint based. In the case of blocked based method, the method is exceptionally good in detecting the forged regions with higher accuracy but uses extremely high power of computation. Speed up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) are invariant with respect to geometrical and illumination transform. In this paper we review the different keypoint approach which is a better approach or alternate to block-based approach due to invariancy property of SURF and SIFT. The keypoint based copy-move forgery detection schemes involve, identifying and relating the localized features of the images using the algorithms like SIFT and SURF.

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.

Copy-move forgery is one of the most commonly used forgeries in digital images, in which some regions of a digital image is coped and pasted to another part of the image with the aim of concealing certain features or objects in the original images. In this paper, a novel copy-move forgery detection scheme in digital images is proposed. It treats the digital images as high dimensional data and uses the intrinsic dimension estimation method first to segment the image and then identify the copy-move forgeries in the image regions with the same texture. Experimental results show that the proposed method is efficient and robust to retouching and many other operations, such as lossy compression, blurring, filtering, etc.

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.

On Image Forgery Detection Using Two Dimensional Discrete Cosine Transform and Statistical Moments

One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.

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.

With the rise of high-quality forged images on social media and other platforms, there is a need for algorithms that can recognize the originality. Detecting copy-move forgery is essential for ensuring the authenticity and integrity of digital images, preventing fraud and deception, and upholding the law. Copy-move forgery is the act of duplicating and pasting a portion of an image to another location within the same image. To address these issues, we propose two deep learning approaches - one using a custom architecture and the other using transfer learning. We test our method against a number of benchmark datasets and demonstrate that, in terms of accuracy and robustness against various types of image distortions, it outperforms current state-of-the-art methods. Our proposed method has applications in digital forensics, copyright defence, and image authenticity.

Feature extraction in Copy-Move Forgery Detection (CMFD) is crucial to facilitate image forgery analysis. Edge detection is one of the processes to extract specific information from Copy-Move Forgery (CMF) Images. It sensitizes the amount of information in the image and filters out useless ones while preserving the important structural properties in the image. This paper compares five edge detection methods: Robert, Sobel, Prewitt (first Derivative), Laplacian, and Canny edge detectors (second Derivatives). CMFD evaluation datasets images (MICC-F220) are tested with both methods to facilitate comparison. The edge detection operators were implemented with their respective convolution masks. Robert with a 2x2 mask, The Prewitt and Sobel with a 3x3 mask, while Laplacian and canny used adjustable masks. These masks determine the quality of the detected edges. Edges reflect a great-intensity contrast that is either darker or brighter.

In this paper, we present a new copy-move forgery detection method based on singular value decomposition (SVD) and the Kolmogorov Smirnov (KS) test. This work introduces a new method of detecting copy-move forgery in images with accuracy up to the pixel level using only 4 features per image block. The proposed method consists of three steps. First, an image is partitioned into blocks of size $$16 \times 16$$ . Second, image features are extracted from each block using steerable pyramid and SVD transforms. Finally, the extracted features are sorted lexicographically and matched using the KS test. The performance of the proposed method is evaluated using the CoMoFoD database. Four post-processing techniques are considered, namely brightness change, contrast adjustment, color reduction, and image blurring. This method achieved a high precision of more than 95% for 3 of the 4 post-processing techniques. The fourth post-processing (i.e., image blurring), we achieved a precision of 75% which is considerably high for such forgery. In addition, the proposed method outperformed published methods when the images were subjected to brightness change, contrast adjustment, color reduction and image blurring. Finally, the performance of the proposed algorithm shown to provide better precision using fewer features compared to several well-known techniques in the literature.

Copy---move image forgery detection has recently become a very active research topic in blind image forensics. In copy---move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy---move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy---move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using "dense" MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy---move image forgery with higher accuracy, especially when the size of the duplicated region is small.

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Due to the availability of various image processing tools forgery over an image can be performed very easily but very difficult to identify. In copy-move forgery, a segment is copied from the original image and pasted at some other location on the same image to hide significant objects of image or to bring additional information which is originally not present in image. Nowadays, this forgery technique is drawing attention. Till now many solutions are presented by researchers to detect such type of forgery in images. Several post-processing operations like rotation, alteration in intensity, noise addition, filtering and blurring can be applied over copy-moved segment which makes detection of forgery very difficult. Copy-move forgery detection is mainly based on finding similarity present in an image and establish a relationship between genuine image parts and pasted portion of the image. This paper is centralized towards providing survey to forgery detection techniques based on different block-based methods. In block-based methods image is divided in blocks of fixed dimension and further features are extracted corresponding to each block of image. Forged blocks are identified utilizing the similarity present between feature vectors.

A robust detection algorithm for copy-move forgery in digital images

Digital images are one of the most regularly used means of storing personal memories and legal proofs. Content modification of digital images is becoming more and more common due to highly advanced image processing tools and techniques. However, digital image modification tools are used to change or hide the digital image content. Copy-move forgery (CMF) is an extensively used image alteration technique in which similar content in that particular digital image alters the contents of the digital image. Many detection methods for CMF have been proposed by various researchers. The keypoint-based CMF detection (CMFD) approach is considered robust and effective. However, the keypoint-based (KB) CMFD approaches are not able to produce satisfactory accuracy, if the tampered regions are small. A novel KB-CMFD approach is presented in this article to detect the small tampered regions in digital images. The CMFD approach proposed in this article is based on the dynamic histogram equalization (DHE), local intensity order pattern (LIOP) as a feature descriptor, and a novel scaled K-means + + (sK-means + +) clustering to detect small regions. The passive KB-CMFD proposed approach performance is evaluated on standard datasets (the CoMoFoD, the MICC-F220, and the defecto MSCOCO (synthetic)). The proposed passive KB- CMFD perform well compared to the other state-of-the-art CMFD approaches.