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Image forgery detection using colour moments
... The detecting CM forgery images are used to overlap square blocks. DCT is used to extract feature vectors from the blocks [10]. ...
... 10) ...
Now a days Image forgery (IF) makes many problems in a society. Super Pixels Segmentation and Scale- Invariant Feature Transform based Image Forgery Detection (SPS-SIFT-IFD) has introduced to detect the forgeries in the images. The forgery region extraction algorithm replaces the features point with small SP as feature blocks and neighboring blocks that have similar local color features has been identified by the Scale-Invariant Feature Transform (SIFT) technique. Artificial Neural Network (ANN) classifier is used for better detection of forged parts in the original images. It applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the SPS-SIFT-IFD scheme achieve much better detection results even under various challenging conditions compared with the existing Forgery Detection (FD) methods. Finally, SPSSIFT- IFD method performance is measure, in terms of Recall, Precision, False Measure, Sensitivity, Sensitivity, Accuracy and Gmean.
... The distinguishing CM fraud pictures are utilized to cover square squares. DCT is utilized to concentrate highlight vectors from the squares [10]. In view of the current investigation, to Develop the IF identification exactness, and limit the period utilization in this study, novel IF discovery MSPS-MSIFT-IFD-SVM procedure is presented. ...
Presently a day's Image fabrications makes numerous issues in the general public. To distinguish the phonies in the pictures Modified Super Pixels Segmentation and Modified Scale-Invariant Feature Transform based Support Vector Machine (MSPS-MSIFT-IFD-SVM) has presented. The fabrication area extraction calculation replaces the highlights point with little MSPS as highlight squares and neighboring squares have comparable nearby shading highlights that is distinguished by the MSIFT system. Bolster Vector Machine (SVM) is utilized for well discovery of fraud slices in the first pictures. It given to the morphological activity to the blended areas to create the identified phony locales. The exploratory outcomes demonstrate that the MSPS-MSIFT-IFD-SVM technique can accomplish worthy location results even under different testing circumstances contrasted with the current falsification identification strategies. MSPS-MSIFT-IFD-SVM technique give in expanding distinctive execution, for example, True positive (TP), True Negative (TN), False Positive (FP), Precision (P), False Negative (FN) Recall (R), Similarity (S), False measure (FM), False Positive Rate (FPR), True Positive Rate (TPR) and more exactness.
... Kemudian untuk nilai pada tiap orientasi sel histogram dilakukan vote bin Vj dan Vj+1 sesuai pada Persamaan (16) dan (17) [21]. Mean, standard deviation, dan skewness pada color moments masing-masing dapat dihitung pada Persamaan (19), (20), dan (21). ...
Fruit is part of a plant that comes from the flower or pistil of the plant and usually has seeds. Meanwhile, vegetables are leaves, legumes, or seeds that can be cooked. Fruits and vegetables have many variants that can be distinguished based on color, shape, and texture. The Saliency-HOG feature and Color moments were used in this study to extract shapes and colors features in fruit and vegetable images. In this study, the Support Vector Machine (SVM) method was used to classify the types of fruit and vegetables. The dataset used in this study is a public dataset consisting of 114 images of fruit and vegetables. Each type of fruit and vegetable contains 100 images consisting of 70 images as training data and 30 images as testing data. There are 4 saliency features used in the testing phase, namely Region Contrast (RC), Frequency-tuned (FT), Histogram Contrast (HC), and Spectral Residual (SR). Based on the test results, the Saliency-HOG and Color Moments features were able to provide good results with the best precision, recall, and accuracy being 98.57%, 98.55%, and 99.120%, respectively.
... Ryu et al. [25] further proposed constructing copy-rotate-move (CRM) detectors for the overlapping blocks. Ustubıoglu et al. [30] proposed calculating RGB color moments and entropy from the overlapping blocks. Yap et al. [31] proposed Polar Harmonic Transforms (PHTs). ...
Copy-move forgery detection can generally be divided into two categories: block-based or keypoint-based methods. However, the existing block-based methods are usually lack of efficiency and the keypoint-based methods have not good detection performance. In this paper, a novel method using the adaptive keypoint filtering and iterative region merging is proposed for copy-move forgery detection. First, a feature extraction algorithm is presented to obtain the candidate keypoint pairs. Subsequently, adaptive keypoint filtering involving adaptive nearest neighbor pair filtering and outlier filtering is proposed to remove the outliers and obtain the inlier (authentic keypoint) pairs. The iterative region merging involving adaptive region iteration and region merging is proposed to iteratively generate more neighboring keypoint pairs and then merge the image segmentations (superpixels) to implement the copy-move region matting. Compared with other state-of-the-art methods, a series of experiments show that the proposed method can overcome defects and achieve better efficiency while keeping the high detection precision in copy-move forgery detection even under conditions that include various post-processing distortions.
... The novel hybrid approach presented with another idea [13,[58][59][60] of comparing triangles instead of matching blocks, or single points. Various Key-point methods are introduced Table 2 gives some different techniques for copy-move forgery detection with their comparison reviews and different datasets used by them. ...
With the extensive use of multimedia on internet and easy approachability of powerful image and video editing software, doctored visual contents have been extensively appearing in our electronic-mail in-boxes, Whatsapp, Facebook or any other social media. Recently, attempting blind tampering in visual contents have been progressively adopted. This paper presents a collaborative survey on detection of such attempts. Our aim is to establish an effective path, for researchers working in the field of image and video forensics, to unfold new aspects of forgery. This paper will avail the comprehensive study that will assist the researchers to go through the various challenges encountered in the previous work. The focus of this paper is to review the splicing and copy–move forgery detection methods in images as well as inter and intra-frame forgery challenges in videos, highlighting the commonly used datasets and hence assisting new researchers to work on with. The efficacy of the paper is that such collaborative survey under one umbrella is not available yet.
... Color moments merupakan salah satu cara untuk mendefinisikan sebuah citra berdarkan distribusi warnanya. Umumnya, color moments memiliki tiga moment utama karena ketiga moment tersebut mampu mengakomodasi informasi warna yang disediakan (Ustubioglu, B., V., & Ulutas, 2015). Penelitian ini menggunakan mean, deviasi standar, dan skewness terdapat pada persamaan (3), (4), dan (5). ...
Abstrak Makanan merupakan kebutuhan utama untuk mempertahankan hidup seorang individu. Kandungan gizi yang terdapat pada makanan yang dikonsumsi memengaruhi performa kerja seseorang dalam menjalankan kegiatannya sehari-hari. Untuk membantu seorang individu dalam mengindentifikasikan makanan mana yang bergizi dan yang tidak maka perlu dibuat algoritme klasifikasi makanan. Visi komputer bisa digunakan untuk mengenali sebuah makanan berdasarkan analisis fitur-fitur tertentu. Penelitian ini menggunakan fitur warna dan tekstur untuk mengklasifikasikan makanan pada citra. Ekstraksi fitur warna menggunakan color moments (CM) melalui ruang warna Red, Green, Blue (RGB), sedangkan Local Binary Patterns (LBP) digunakan untuk proses ekstraksi fitur tekstur. Algoritme klasifikasi menggunakan algoritme K-Nearest Neighbors (K-NN). Hasil pengujian variasi nilai k dan kombinasi fitur LBP dan CM, LBP saja, dan CM saja, menunjukkan bahwa hasil evaluasi f1-score tertinggi adalah 0,89 ketika menggunakan fitur warna dari color moments saja dengan nilai k=1. Maka algoritme klasifikasi dapat bekerja paling efisien pada dataset ini jika menggunakan fitur warna saja dengan metode klasifikasi k-NN. Kata kunci: klasifikasi makanan, color moments, local binary patterns, k-NN. Abstract Food is a primary need to help individuals in executing their daily activities. The nutritional value provided by certain food items affects one's performance in executing their daily activities. Individuals need to be assisted in identifying what food items are nutritious and those that are not, hence a classification algorithm is made for this task. Computer vision can be utilized to classify food items based on analyzing certain features. This research uses color and texture features to classify food items that are in images. Color feature extraction utilizes Color Moments (CM) using a Red, Green, and Blue (RGB) color channel, while Local Binary Patterns (LBP) is utilized for texture feature extraction. The k-Nearest Neighbors (k-NN) is used for the classification process. Varying the values of k in the k-nearest neighbors algorithm during testing and combinatios of features used, showed that the highest value for f1-score during evaluations was 0,89 when the value of k=1 and when only the color features from using color moments were used. Therefore the classification algorithm works efficiently on the dataset used in the research if only color features were used using k-NN as the classification algorithm. 1. PENDAHULUAN Manusia memiliki tiga kebutuhan primer yakni sandang, pangan, dan papan. Pangan merupakan kata lain dari sesuatu yang dapat dikonsumsi oleh manusia. Pangan dapat berbentuk makanan maupun minuman yakni untuk dikonsumsi untuk dijadikan sumber energi dalam beraktivitas sehari-hari. Sehingga jika kebutuhan ini tidak terpenuhi maka jelas seorang individu tidak akan bisa melanjutkan kegiatan sehari-harinya dengan maksimal. Sebuah produk pangan bisa dikategorikan layak dikonsumsi jika memiliki kandungan gizi yang cukup serta tidak menimbulkan efek samping negatif jika sudah selesai dikonsumsi.
... Kashyap et al. [10] presented a new block-based method for CMFD by using wavelet decomposition and employing blur moment invariants to represent of each block feature. Ustubıoglu et al. [17] assumed that the color distribution of each image block is stabilize and presented RGB color moments to extract feature vectors from the image blocks. Kpa et al. [9] presented Zernike Moments to extract the feature vectors of image blocks and employed Discrete Wavelet Transform (DWT) to reduce high-dimension. ...
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.
The problem of picture fraud has become more pervasive in the current digital era as a direct result of the ease with which complex image manipulation programs can be accessed. This research study provides a comprehensive analysis of several methodologies and approaches that are used to detect and localize cases of picture fraud. The primary objective is to provide a clear understanding of the most cutting-edge methods that are now being utilized, as well as the problems and opportunities that lie ahead. In the field of picture forensics, the focus of this article is on several distinct types of image fraud, such as copy-move, splicing, retouching, and inpainting forgeries. We study the underlying principles that lie behind frequently used detection methods, such as block matching, feature-based analysis, and approaches that are anchored in deep learning. In addition, we go over the benefits and drawbacks of each strategy, focusing on how they apply to a variety of settings. In addition, this article takes a look at the datasets that are typically utilized for the training and evaluation of forgery detection algorithms, highlighting both the benefits and the limitations of those datasets. In addition to this, we examine the numerous assessment metrics that are utilized in order to evaluate the performance of the various methods, with a particular focus on the requirement for standardized benchmark datasets and evaluation methodologies. In addition, we discuss the obstacles that are presented when attempting to identify picture fraud in the real world. These issues include the need to deal with photos that have been compressed, images that have varying resolutions, and the existence of post-processing effects. In this article, we will discuss the significance of multi-modal analysis and the fusion of information obtained from a variety of sources in order to improve the reliability of counterfeit detection systems. The approaches that are used to detect picture counterfeiting are the topic of the next section of this review. We look at several techniques, such as those based on segmentation, texture analysis, and deep neural networks, in order to determine the precise position of forged sections inside a picture. We go through their precision, the amount of computing complexity they have, and the possible uses they have. In conclusion, we discuss the potential of the future. In order to identify and localize forged images, it is necessary to keep up with the latest innovations in image editing software and build forgery detection systems that are more technologically advanced and effective. This is because picture editing techniques are always being refined and improved. We offer prospective research avenues, such as Explainable AI, Generative Adversarial Networks (GANs) for forgeries production, and hybrid techniques to combine the strengths of various detection methods. Specifically, we focus on hybrid approaches to combine the strengths of diverse detection methods. In conclusion, the purpose of this study is to offer academics and industry professionals in This article provides a comprehensive review of the present state of identifying and localizing picture forgeries, which falls under the umbrella of image forensics as a discipline. It encompasses the most recent developments in forgery detection and localisation, making it possible to gain an in-depth comprehension of the subject matter. It is possible to pave the way for more effective and reliable forgery detection systems to protect the integrity of digital pictures in a variety of applications if we grasp the existing approaches as well as the obstacles that they provide.
Copy-move forgery is one of the well-known image forgery technique which exploits regions of the same image to create forged image by replicating or hiding authentic content of the original image. Original images can also contain similar looking but authentic objects. In such cases, identification of authentic and tampered images is a complicated task. To tackle this problem, we propose a method in which Stationary Wavelet Transform (SWT) and spatial-constrained edge preserving watershed segmentation are applied over input image in preprocessing step. Keypoint extraction and descriptor computation are performed using Cascaded Features from Accelerated Segment Test (Cascaded FAST) and Binary Robust Invariant Scalable Keypoint (BRISK) descriptor, respectively. Approximate nearest neighbor search is performed using Random Binary Search Tree (RBST) method. For keypoint clustering, Adaptive Density Peak Clustering (ADPC) technique is employed. Outlier removal is performed using Random Sample Consensus (RANSAC) technique. Further, forged regions are localized using correlation map generation. Experimental results display that the proposed approach can effectively distinguish between forged and original images containing similar appearing but authentic objects. It is also able to detect forged images sustaining different post-processing attacks. For COVERAGE dataset, proposed technique achieves high F-Measure = 86.901% and low False Positive Rate (FPR) = 15.241% in comparison to state-of-the-art techniques.
Most of the existing forensic methods aimed at detecting duplicated regions in images are sensitive to geometrical changes in the replicated areas. Consequently, a simple re-flection/rotation of the copied region could be used to not only suit the scene of the image, but also hinder its proper de-tection. In this paper, we propose a novel forensic method to detect duplicated regions, even when the copied portion have experienced reflection, rotation or scaling. To achieve this, overlapping blocks of pixels are re-sampled into log-polar coordinates, and then summed along the angle axis, to obtain a one-dimensional descriptor invariant to reflection and rota-tion. Moreover, scaling in rectangular coordinates results in a simple translation of the descriptor. This approach allows us to perform an efficient search of similar blocks, by means of the correlation coefficient of its Fourier magnitudes. Re-sults are presented to demonstrate the effectiveness of the proposed method.
One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.
Copy-move forgery is a specific type of image tampering, where a part of the image is copied and pasted on another part of the same image. In this paper, we propose a new approach for detecting copy-move forgery in digital images, which is considerably more robust to lossy compression, scaling and rotation type of manipulations. Also, to improve the computational complexity in detecting the duplicated image regions, we propose to use the notion of counting bloom filters as an alternative to lexicographic sorting, which is a common component of most of the proposed copy-move forgery detection schemes. Our experimental results show that the proposed features can detect duplicated region in the images very accurately, even when the copied region was undergone severe image manipulations. In addition, it is observed that use of counting bloom filters offers a considerable improvement in time efficiency at the expense of a slight reduction in the robustness.
We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 -, L 2 -, or L1 -distance between two cumulative color histograms can be used to define a similarity measure of these two color distributions. We show that while this method produces only slightly better results than color histogram methods, it is more robust with respect to the quantization parameter of the histograms. The second technique is an example of a new approach to color indexing. Instead of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which is used for the retrieval is a weighted sum of the absolute differences between corresponding mo...
We describe an efficient technique that automatically detects duplicated regions in a digital image. This technique works by first applying a principal component analysis to small fixed-size image blocks to yield a reduced dimension representation. This representation is robust to minor variations in the image due to additive noise or lossy compression. Duplicated regions are then detected by lexicographically sorting all of the image blocks. We show the efficacy of this technique on credible forgeries, and quantify its robustness and sensitivity to additive noise and lossy JPEG compression. the Office for Domestic Preparedness, U.S. Department of Homeland Security (points of view in this document are those of the authors and do not necessarily represent the official position of the U.S. Department of Homeland Security).
The presence of duplicated regions in the image can be considered as a tell-tale sign for image forgery, which belongs to the research field of digital image forensics. In this paper, a blind forensics approach based on DWT (discrete wavelet transform) and SVD (singular value decomposition) is proposed to detect the specific artifact. Firstly, DWT is applied to the image, and SVD is used on fixed-size blocks of low-frequency component in wavelet sub-band to yield a reduced dimension representation. Then the SV vectors are then lexicographically sorted and duplicated image blocks will be close in the sorted list, and therefore will be compared during the detection steps. The experimental results demonstrate that the proposed approach can not only decrease computational complexity, but also localize the duplicated regions accurately even when the image was highly compressed or edge processed.
As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most
commonly used methods, copies a part of the image and pastes it into another part of the the image. In this paper, we propose
a detection method of copy-move forgery that localizes duplicated regions using Zernike moments. Since the magnitude of Zernike
moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated.
Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring.
Experimental results demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move
forgery.
Techniques for digital image tampering are becoming more and more sophisticated and widespread. Copy-move forgery is one of the tampering techniques that are frequently used. In this paper, an improved DCT-based method is developed to detect this specific artifact. Firstly, the image is divided into fixed-size overlapping blocks and, DCT is applied to each block to represent its features. Truncating is employed to reduce the dimension of the features. Then the feature vectors are lexicographically sorted and, duplicated image blocks will be neighboring in the sorted list. Thus duplicated image blocks will be compared in the matching step. To make the method more robust, a scheme to judge whether two feature vectors are similar is imported. Experiment results demonstrated that the proposed method can detect the duplicated regions even when an image was distorted by JPEG compression, blurring or additive white Gaussian noise.