A new approach for detecting copy-move forgery in digital images

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.

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

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.

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

Copy-move forgery is one of the types of image manipulation which is widely used due to simplicity and effectiveness. In this method, part of the original image is copied and pasted to the desired location in the same image. The goal of detecting copy-move forgery is to find areas of the image that are identical or very similar. One of the important issues that some of the earlier algorithms suffer from is that the forged area is rotated or resized after attachment. In this research, a new approach is presented to detect copy-move forgery in digital images based on discrete wavelet decomposition along with multiple features extracted by Gabor filter to improve the function of detecting similar areas of the image. Experiments have shown that this algorithm recognizes similar areas with relatively good accuracy and is resistant to rotation and change in the scale of the forged area.

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.

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.

Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor

One of the common image forgery techniques is the splicing, where parts from different images are copied and pasted onto a single image. This paper proposes a new forensics method for detecting splicing forgeries in images containing human faces. Our approach is based on extracting an illumination-signature from the faces of people present in an image using the dichromatic reflection model (DRM). The dichromatic plane histogram (DPH), which is calculated by applying the 2D Hough Transform on the face images, is used as the illumination-signature. The correlation measure is employed to compute the similarity between the DPHs obtained from different faces present in an image. Finally, a simple threshold on this similarity measure exposes splicing forgeries in the image. Experimental results show the efficacy of the proposed method.

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.

One of the most popular image forgery technique is copy-move forgery. In this technique, one or more segments are copied and affixed at different positions within the image. This forgery technique is highly grievous as it can manipulate an image in various ways (such as by presenting additional information or by concealing the genuine information of image). We propose a novel blind forensic technique for copy-move image forgery detection. Our approach utilize different edge detection operators to extract high frequency features. Histogram of Oriented Gradients (HOG) and Weber Local Descriptor (WLD) are used to extract image block features. Radix and lexicographical sorting is enforced over feature vector matrix followed by correlation computation between feature vectors to detect similar feature vectors. Shift vectors are computed to locate similar group of blocks within image. Connected component labeling is applied as morphological operation to remove false matches. Proposed approach is robust to detect plain as well as multiple copy-move forgery in images with post-processing attacks such as contrast adjustment, image blurring, color reduction, and brightness change. Proposed approach achieve highest F-Measure(%) in comparision to other existing forgery detection methods.

In today's modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

Copy-move image forgery detection based on Gabor magnitude

Digital image forgery, the manipulation of images to conceal or falsify information, has become increasingly prevalent with the advent of advanced image editing tools. Copy-move forgery, in particular, remains one of the most common techniques used for tampering. This paper presents an Image Manipulation Detection System capable of identifying multiple types of image forgeries, including double JPEG compression, copy-move forgeries, metadata inconsistencies, Color Filter Array (CFA) artifacts, and noise variance irregularities. The system integrates multiple forensic techniques such as error level analysis, string extraction, and image extraction into a unified platform with a graphical user interface. The tool aims to support forensic investigations, journalistic verification, and digital evidence validation by providing a comprehensive, easy-to-use detection framework.

With billions of digital images flooding the internet which are widely used and regards as the major information source in many fields in recent years. With the high advance of technology, it may seem easy to fraud the image. In digital images, copy-move forgery is the most common image tampering, where some object(s) or region(s) duplicate in the digital image. The important research has attracted more attention in digital forensic is forgery detection and localization. Many techniques have been proposed and many papers have been published to detect image forgery. This paper introduced a review of research papers on copy-move image forgery published in reputed journals from 2017 to 2020 and focused on discussing various strategies related with fraud images to highlight on the latest tools used in the detection. This article will help the researchers to understand the current algorithms and techniques in this field and ultimately develop new and more efficient algorithms of detection copy-move image.