Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images

The iris is a part of the human anatomy that can be used as a biometric identifier. Data obtained from the iris can be converted into information through iris image processing, and in order to obtain accurate iris pixel results, an edge detection operator is required that can provide detailed and good image quality effects. In this research, a comparative analysis of the Canny, Sobel, Prewitt and Roberts edge detection operators was carried out on iris images. The purpose of performing a comparative analysis of edge detection methods is to compare the detection results of each edge detection operator on iris recognition detected by each operator. The results of the comparison of edge detection methods using precision tables can be analyzed to show that the Canny edge detection operator provides better, smoother and sharper edge results in actual edge point detection, namely 0.357867, while Sobel =, 0.210212, Prewitt = 0.212452 and Roberts = 0.279196. From these results it can be concluded that the edge detection results provided by Sobel and Prewitt are less sharp and sensitive to noise, and the comparison results can vary depending on the intensity of the image and the image object being compared.

Edge Detection is a very essential part in digital image processing. In case of applications which require object information or feature extraction in an image, edge detection can play a vital role. Edge occurs where there is discontinuity or a sharp change in the intensity function. Now there are many operators for edge detection, but the challenge is to get better results from the existing system. This paper represents a hybrid approach which combines the Sobel and Canny edge detectors. It also uses a median filter to remove the salt and pepper noise from the image which consequently smoothen the image and edges can be detected easily. A comparison has also been shown between the hybrid approach with median filter and without median filter. The comparative study shows that using median filter shows enhanced result. As it filters the noise from the image, the accuracy of edge detection improved and achieved ideal effect.

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.

Abstract: Abstract: this study evaluates and compares the performance of two edge detection methods Sobel method and Canny method on batik image. Batik images have unique characteristics and complex patterns, making it difficult to analyze the edges. This study presents a comparison of the results using sobel and canny edge detection methods on batik kawung images both from peak signal-to-noise reatio and from mean squared error. The results showed that canny edge detection was better than sobel method. This can be seen from the results of PSNR and MSE that is 100%. This analysis is determined by considering factors such as the accuracy of edge detection, sensitivity to noise, and the ability to handle the complexity of batik drawing patterns. The results of this study provide a detailed description of the advantages and disadvantages of each method in the image of batik kawung. The conclusions that can be drawn from this study can provide valuable guidance for choosing the optimal edge detection method in image analysis of batik kawung and others. Keywords: batik kawung; canny; MSE; PSNR; sobel Abstrak: Penelitian ini mengevaluasi dan membandingkan kinerja dua metode deteksi tepi metode Sobel dan metode Canny pada citra batik. Gambar batik mempunyai ciri-ciri yang unik dan pola yang kompleks, sehingga menyulitkan analisis tepian. Penelitian ini menyajikan perbandingan hasil menggunakan metode deteksi tepi sobel dan canny pada citra batik kawung baik dari peak signal-to-noise reatio maupun dari mean squared error. Hasil penelitian ini menunjukkan bahwa deteksi tepi canny lebih baik dibandingkan dari metode sobel. Hal ini dapat dilihat dari hasil PSNR dan MSE yang dihasilkan yaitu 100%. Analisis ini ditentukan dengan mempertimbangkan faktor-faktor seperti keakuratan deteksi tepi, kepekaan terhadap noise, dan kemampuan menangani kompleksitas pola gambar batik. Hasil penelitian ini memberikan gambaran secara detail mengenai kelebihan dan kekurangan masing-masing metode pada citra batik kawung. Kesimpulan yang dapat diambil dari penelitian ini dapat memberikan panduan berharga untuk memilih metode deteksi tepi yang optimal dalam analisis citra batik kawung dan yang lainnya. Kata kunci: batik kawung; canny; MSE; PSNR; sobel

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.

In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.

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.

Binocular vision ranging is becoming more and more widely used. In this paper, based on the principle of binocular camera ranging, a binocular ranging system is built with the help of Win10+VS2017+0penCV3.4.6. The calibration of the binocular camera is completed using the Zhang Zhengyou chessboard calibration method and Matlab2017b calibration toolbox. On the basis of BM matched ranging, the method of Canny edge detection, Sobel edge detection and Scharr filter edge detection edge detection followed by BM matched ranging was proposed to obtain the 3D information of the target point on the parallax map, and the difference of the effective target point to the x coordinate was the length of the measured paper. The experimental results demonstrate that the canny edge detection followed by BM matching distance measurement method yields more accurate results with a smaller error of 1.6% or less.

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;

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.

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

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.

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.