Image is a powerful communication tool that is widely used in various applications, such as forensic medicine and court, where the validity of the image is crucial. However, with the development and availability of image editing tools, image manipulation can be easily performed for a specific purpose. Copy-move forgery is one of the simplest and most common methods of image manipulation. There are two traditional methods to detect this type of forgery: block-based and key point-based. In this study, we present a hybrid approach of block-based and key point-based methods using meta-heuristic algorithms to find the optimal configuration. For this purpose, we first search for pair blocks suspected of forgery using the genetic algorithm with the maximum number of matched key points as the fitness function. Then, we find the accurate forgery blocks using simulating annealing algorithm and producing neighboring solutions around suspicious blocks. We evaluate the proposed method on CoMoFod and COVERAGE datasets, and obtain the results of accuracy, precision, recall and IoU with values of 96.87, 92.15, 95.34 and 93.45 respectively. The evaluation results show the satisfactory performance of the proposed method.

One of the challenges today is capturing fake images. One type of image forgery is a Copy-move forgery. In this method, a part of the image is Copy and placed at the most similar point. Due to the existing algorithms and processing software, it is not easy to detect forgery areas and has created challenges in various applications. Based on the Bat algorithm, the proposed method has tried to help detect fake images by finding forgery areas. The proposed method includes a simple image segmentation and detection of forgery areas with the help of the BAT Algorithm with Mutation. According to the proposed algorithm, the image is first grayed out, then divided into 100 pieces. The optimal Bat algorithm randomly selects some component image and performs a similarity search. The mutation operator is used to avoid getting stuck in the local optimization. The proposed algorithm does not get stuck in the local optimization with the help of the mutation operator and can find forgery areas with a precision of about 81.39% for the IMD dataset and about 81.04% for the MICC-F600 dataset.

Image forgery, the manipulation of an image to hide some meaningful or helpful information, is widely used to manage the large amount of information being exchanged in the form of images. There are different forms of image forgery, and Copy--move forgery is the most common form of it. The Copy-move forgery is easy to perform yet challenging to detect due to the similarity between the original part of the image and the copied part. In this paper, we employ a keypoint descriptor inspired by the human visual system, namely the FREAK (Fast Retina Keypoint) descriptor, for robust Copy-move forgery detection. This method uses the advantages of FREAK descriptor such as fast computing and low memory load compared to SIFT, SURF, and BRISK. Finally, geometric transformation parameters are extracted and discussed. Results confirm promising results in the case of image post-processing operations such as adding noise, illumination change, and geometric transformations such as rotation and scaling.

Today, due to the advent of the powerful photo editing software packages, it has become relatively easy to create forgery images. Recognizing the correctness of digital images becomes important when those images are used as evidence in legal, forensic, industrial, and military applications. One of the most common ways to forge images is Copy move forgery, in which one part of the image is copied and pasted in another part of the same image. So far, various methods have been proposed for detecting Copy move forgery, but these methods are not able to detect Copy move forgery with different challenges of noise, rotation, scale, and detection of symmetrical images with high accuracy. In this paper, an enhanced hybrid method based on local and frequency feature extraction is presented for image Copy move forgery detection, which has a very high resistance to above challenges, both individually and simultaneously and has provided good identification accuracy. In this method, the combination of Discrete Wavelet Transform, Scale Invariant Feature Transform and Local Binary Pattern are used simultaneously. The forged area is chosen in such a way that at least both methods used in this proposed method have consensus about the forgery of that area. Various experiments and analyses on the MICC database show that the proposed methods, despite the above challenges, have reached the accuracy of 98. 81% both separately and simultaneously, which shows significant improvement compared to other methods used in this field.

Increased popularity of digital media and image editing software has led to the spread of multimedia content forgery for various purposes. Undoubtedly, law and forensic medicine experts require trustworthy and non-forged images to enforce rights. Copy-move forgery is the most common type of manipulation of digital images. Copy-move forgery is used to hide an area of the image or to repeat a portion in the same image. In this paper, a method is presented for detecting Copy-move forgery using the Scale-Invariant Feature Transform (SIFT) algorithm. The spearman relationship and ward clustering algorithm are used to measure the similarity between key-points, also to increase the accuracy of forgery detection. This method is invariant to changes such as rotation, scale change, deformation, and light change; it falls into the category of blind forgery detection methods. The experimental results show that with its high resistance to apparent changes, the proposed method correctly detects 99. 56 percent of the forged images in the dataset and reveals the forged areas.

Introduction: forgery is a reprehensible and immoral behavior that usually aims at financial gain, but may be done to influence the opinion of one person, deceive another, or simply to cause harm. Given the technological developments and the expansion of the use of digital tools, computer forgery has become one of the serious challenges in legal systems. Therefore, the aim of the present study is to examine the ethical dimensions of forgery and comparative analysis of computer forgery and traditional forgery in Iranian and Iraqi law. Material and Methods: This study used reliable sources and international scientific articles in the field of ethical consideration of forgery and comparative analysis of computer forgery and traditional forgery. Conclusion: Since the emergence of writing and the beginning of the use of writing to convey intentions and thoughts, distortion and alteration of lines and words have been recognized as immoral, undesirable and criminal acts. In Islamic law, forgery is recognized as one of the examples of Ta’zir crimes, and the Holy Quran has also considered this act immoral and reprehensible and has included it among the sins. With the comparative analysis presented regarding computer forgery and electronic documents in Iranian and Iraqi law, general and practical conclusions can be reached. This study showed that both legal systems, despite similarities in the main concepts and objectives of the laws, have different approaches in formulating and implementing regulations related to forgery and electronic documents.