PSO and SURF based digital image forgery detection

Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

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.

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

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.

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.

The increasing accessibility of digital imaging technology has led to a rise in image forgery, raising concerns about digital content authenticity in forensic and security domains. Copy-move forgery is one the most prevalent and challenging forgery techniques due to its seamless manipulation. We propose a novel passive CMFD (CMFD) approach that leverages a modified Kirsch (mKirsch) edge detector and a modified SIFT-based descriptor (DivSIFT) to accurately identify and localize copy-move forgery (CMF). The mKirsch edge detector enhances edge detection by selectively deleting specific masks, improving keypoint extraction and feature matching. We used MICC-F220, CoMoFoD, and MICC-F8Multi datasets to measure the performance of the new method, under challenging conditions such as rotation, scaling, JPEG compression, and multiple forgeries. The results show that mKirsch-enhanced detection outperforms compared to conventional Kirsch-based methods. Notably, methods with deleted masks (WW_NW and NE_SE) achieved a True Positive Rate (TPR) of 90.91%, precision 100%, and an F-measure of 95.24%. Robust against rotation and scaling attacks, achieving a TPR of up to 96.97% with zero false positives. Additionally, the method is computationally efficient, with an execution time of 2.74 seconds, making it suitable for real-world applications. These findings establish the mKirsch-based CMFD as a highly accurate and efficient solution for image forgery detection in digital images.

In this paper, copy-move forgery in image is detected for single image with multiple manipulations such as blurring, noise addition, gray scale conversion, brightness modifications, rotation, Hu adjustment, color adjustment, contrast changes and JPEG Compression. However, traditional algorithms detect only copy-move attacks in image and never for different manipulation in single image. The proposed LLP (Laterally linked pixel) algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detection of copied region and kernel tricks is applied for detection of multiple manipulations in single forged image. LLP algorithm consists of two channels such as feeding component (F-Channel) and linking component (L channel) for linking pixels. LLP algorithm linking pixels detects image with multiple manipulation and copy-move forgery due to one-to-one correspondence between pixel and neuron, where each pixel’s intensity is taken as input for F channel of neuron and connected for forgery identification. Furthermore, neuron is connected with neighboring field of neuron by L channel for detecting forged images with multiple manipulations in the image along with copy-move, through kernel trick classifier (KTC). From experimental results, proposed LLP algorithm performs better than traditional algorithms for multiple manipulated copy and paste images. The accuracy obtained through LLP algorithm is about 90% and further forgery detection is improved based on optimized kernel selections in classification algorithm.

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Copy-move image forgery detection based on Gabor magnitude

This paper presents a highly effective and reliable approach for detecting image forgery and identifying manipulated regions in digital images. The proposed method uses a combination of Singular Value Decomposition (SVD) and the Speeded-Up Robust Features (SURF) algorithm, achieving a high degree accuracy of 99.1% for revealed tampering. After an input image is initially divided parallel to partition, then is performed by SVD to extract features with remarkable discriminability, the method is valued based on independent experiments. The norms are calculated, and pixels with the same norm begin to group to identify potentially tampered areas. In order to simplify the detection process, we conduct a weighted comparison among subgroups to distinguish real structures from false ones. Once we discover a suspicious forgery area, the SURF algorithm comes into play to accurately identify the manipulated items. This process uses a keypoint detector, descriptor calculations, the match between points, and geometric checking to improve the accuracy and reliability of forgery localization. Experimental results on different image databases show that this method is effective. It exhibits advanced ability in detecting forgeries, finding objects and locating where they are in an image. Eventually, we hope this work will produce a sturdy forgery detection system and improve the accuracy of recognizing tampered regions. The proposed method is useful in digital forensics and image verification.

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.

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.

Detection of facial forgery in images and videos is a complex and challenging task. For the last years, different methods have been employed to detect facial forgeries. However, facial forgeries are still very challenging to detect. In this paper, a modern facial forgery detection method is presented which automatically detects facial forgeries in videos and images. The proposed method classifies fake and original videos and images. To classify the images, deep learning model, Inception-ResNet has been adopted. The proposed method is validated on publicly available Deepfake TIMIT dataset and effective results have been reported.

Image forgery detection by transforming local descriptors into deep-derived features

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.