Utilization of edge operators for localization of copy-move image forgery using WLD-HOG features with connected component labeling

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.

Images are one of the most prominently used digital information sharing medium now a days. Due to availability of state-of-the-art image editing tools it has become very easy to forge an image. Among various types of image forgeries, copy-move (region-duplication) forgery cases are emerging very frequently. In copy-move image forgery one or more regions of an image are replicated within the same image. In this paper, a new robust copy-move image forgery detection technique is proposed using Gaussian-Hermite Moments (GHM). The proposed technique divides the input image into overlapping blocks of fixed size and then the Gaussian-Hermite moments are extracted for each block. The matching of similar blocks is done by sorting all the features lexicographically. The experimental results show that the proposed technique can locate the copy-move forged regions in a forged image very accurately. The proposed technique shows promising results in the presence of various post-processing operations scaling, blurring, color reduction, adjustment of brightness, rotation, and JPEG compression.

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.

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.

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.

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

The copy-move forgery is a common way to manipulate the digital images, which copied regions come from the same image. The detection process of altered regions is known as forgery detection. In this paper, we propose a new block-based approach for detecting copy-move forgery in digital images. The novelty of the proposed approach is the usage of structural similarity index as the method for similarity matching step. Additionally, Gaussian pyramid decomposition method allows us to improve run-time speed of the approach considerably. Therefore, feature extraction would not require, hence; it can help to diminish time-consuming issue. Accordingly, simplicity in calculating and analyzing is the significant specification of the proposed approach. Experiments are provided to illustrate an efficiency of suggested approach and evaluate its robustness and sensitivity against some post-processing operations.

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.

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

Copy-move is one of the most common image forgeries, wherein one or more region are copied and pasted within the same image. The motivations of such forgery include hiding an element in the image or emphasizing a particular object. Copy-move image forgery is more challenging to detect than other types, such as splicing and retouching. Keypoint based copy-move forgery detection extracts image keypoints and uses local visual features to identify duplicated regions, which exhibits remarkable performance with respect to memory requirement and robustness against various attacks. However, these approaches fail to handle the cases when copy-move forgeries only involve small or smooth regions, where the number of keypoints is very limited. Also, they generally have higher time costs owing to complex feature descriptor and more error matching points. To tackle these challenges, we propose a fast and effective copy-move forgery detection method through adaptive keypoint extraction and processing, introducing fast robust invariant feature, and filtering out the wrong pairs. Firstly, the uniform distribution keypoints are extracted adaptively from the forged image by employing the fast approximated LoG filter and performing the uniformity processing. Then, the image keypoints are described using fast robust invariant feature and matched through the Rg2NN algorithm. Finally, the falsely matched pairs are removed by employing the segmentation based candidate clustering, and the duplicated regions are localized using optimized mean-residual normalized production correlation. We conduct extensive experiments to evaluate the performance of the proposed scheme, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.

Copy-move image forgery detection based on Gabor magnitude

Copy-Move Image Forgery is a clean and efficient method to perform digital image forgeries. To localize a forged region, is an important task in detecting such kind of forgeries. This paper proposes a key point based passive image forensic technique to detect and localize forged regions in a manipulated image. Key points are extracted from the image using Harris Corner detector and a specific area around each key point is used for feature extraction using BRISK. The Hamming distance metric is used to detect the distances among the features obtained to facilitate the measurement of similarity. Similarity among feature vectors is detected using Nearest Neighbor Distance Ratio. Finally outliers are removed using RANSAC. The experiment results reveal the effectiveness of the proposed method to general geometrical transformations and post processing operations.

Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

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.

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.