Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

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.

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

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 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.

This work presents a copy-move forgery detection method based on blobs detection from canny edge-response images, followed by SIFT (Scale Invariant Feature Transform) key points analysis. The SIFT key points are extracted from grayscale images, which are then clustered according to their distance from within generated blobs of that image. Different key points in different blobs are ignored (unique non-duplicated key points) while the same key points in different blobs are matched. The assumption made here is that these duplicate regions are highly indicative of forgery. The proposed method is tested on the MICC-F220, MICC-8Multi, and CoMoFoD public benchmark datasets. The results demonstrate that copy-move forgery detection can be reliably performed on geometrically transformed images, i.e., rotated, scaled, combined and post-processed images such as JPEG compression, blurred, noise added, and contrast adjusted. Our method is also robust on multiple copy-move forgeries.

In ground-based remote sensing cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when features are extracted and classification is performed on the basis of the original images, a high-resolution probably means a high (or even more, unacceptable) computation cost. In practical application, a simple and commonly adopted method is to appropriately resize the original image to a version with a decreased resolution. An inevitable problem is whether useful information is lost in this resizing operation. This paper demonstrates that information loss is inevitable and poor classification results may be obtained from the analysis of local binary pattern (LBP) histogram features. However, this problem has been always neglected in previous studies, and the original image is arbitrarily resized without any criterion. In particular, the histogram features based on LBPs actually reflect the distribution of features. Thus, a criterion based on the Kullback–Leibler divergence between LBP histograms from the original and resized images and a penalty term imposed on the resolution are proposed to select the resolution of the resized image. The optimal resolution of the resized image can be selected by minimizing this criterion. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion by analyzing the LBP histogram features.

A face tracking algorithm based on LBP (Local Binary Pattern) histograms and particle filtering is presented in this paper. The mean shift algorithm is adopted to locate the face region coarsely. Then the particles can be propagated efficiently with small random noise added. Since color histograms as face representation can be disturbed easily by the background with skin color. LBP histograms are used as the representation of faces. In order to alleviate the influence of light variance, the face template is updated under certain condition. The experimental results show that the proposed algorithm is superior to the CAMSHIFT algorithm and the particle filter based on color histograms.

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.

Biometrics have been widely used for human identification, including fingerprint, face and iris because they cannot be easily duplicated. Recently, biometrics have been also used for animal (i.e. cattle in this paper) identification. Individual cattle identification is necessary for many important reasons including determining legal ownership, verifying transferred source, and implementing disease surveillance and control. Popular traditional methods for individual cattle identification are using plastic ear tags or microchips. However, the tag can be deduplicated and it can be dangerous and takes time for the human expert to place the microchip in the cattle. Also, it may hurt the cattle. Thus, in this paper, muzzle print is used as a biometric for automatic cattle identification. The fusion of texture features extracted from the muzzle image is used to represent individual cattle. They are Gabor feature and Local Binary Pattern (LBP) histogram. Gabor features were extracted at the different scales and orientations in specific frequencies, while LBP histogram was extracted for each local sub-image to preserve local spatial textures. Then, Support Vector Machine (SVM) is employed as a classifier. The proposed method is reported with the perfect accuracy.

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

SIFT (scale invariant feature transform) is one of the most robust and the widely used image matching algorithms based on local features. It has been used in the aspect of the cameras target matching. For its high complexity, this paper proposes a target matching method for multiple cameras by combining SIFT and the rotation-invariant LBP (local binary patterns). The object detection is obtained by improved Back-ground subtraction technique, and particle filter is used for region-based object tracking. The descriptor combining SIFT and the rotation-invariant LBP is calculated for every object region. The object matching is obtained by comparing the distance of feature matrix. Experimental results show that the proposed algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm, and it is robust in various scenes.

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.

Copy-Move Forgery Detection (CMFD) method is useful for identifying copy and pasted portions in an image. CMFD has demand in forensic investigation, legal evidence and in many other fields. In this paper, the gists of different newly arrived methodologies in current literature are discussed. Some existing methodologies can be able to localize the forged region and some are not. An efficient method for localization of copy move forgery is proposed in this work for identifying forgery. In the proposed methodology, CMFD is achieved by giving suspected image to Steerable Pyramid Transform (SPT), Local Binary Pattern (LBP) is applied on each oriented subband obtained from SPT to extract feature set, then it is used to trained Support Vector Machine (SVM) to classify images into forged or not. Then localization process is carried out on forged images. Results of proposed methodology are showing robustness even though the forged image has undergone some post processing attacks viz., rotation, flip, JPEG compression.

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.

Digital images and videos play the most important role in digital forensics. They are the prime evidences of any crime scene. Among all classes of image forgeries region-duplication is the simplest and most widely launched technique to forge an image. Region duplication is also known as copy-move forgery. In this type of forgery one part of the image is copied and then pasted into another part of the same image. In this paper we particularly deal with copy-move form of digital image forgery. The existing techniques for identifying copy-move forgery are based on exact block matching approach. However, the existing techniques detect an image forgery when the copied region(s) is/are directly moved to another location of the image, without undergoing any geometric transformation. In this paper we propose a technique to detect geometrically transformed copy-moved image regions. Specifically we aim to detect rotation, rescaling, as well as a combination of both. We use Scale Invariant Feature Transform (SIFT) for our purpose.