# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 1 Introduction  
Nowadays, manipulating digital images has become very simple due to powerful computers, advanced image editing software, and high-resolution imaging tools. The development of image processing software, tools, and techniques has increased the number of fake images that cannot be easily recognized without special tools [1]. As digital images become more widespread daily, tools for distorting and falsifying digital images also become more available. Therefore, there is a need for methods that can detect the forgery of images. Some methods have been proposed to achieve this goal [2]. In some methods, information is embedded in the image from the beginning so that it can be examined when necessary and the image's authenticity can be determined. These methods are called active detection methods. Nevertheless, recognizing the originality of the image without the need for previous embedded information or the primary content of the image has been an important research topic in recent years. These methods are known as passive or blind methods [1]. With the growth of image processing software, many fake images have been created. In most cases, these fake images cannot be recognized by the human eye and therefore require image forgery detection algorithms. An image forgery detection algorithm should be ableto make a correct decision regarding its authenticity without needing basic information about the image and its content. The need to detect image forgery is felt in many fields, such as legal-judicial investigations, photography of publications, news agencies, criminal investigations, insurance companies, medical images, scientific societies, etc. [3]. Researchers have proposed methods to detect different types of image forgery; however, most are designed to detect one type of forgery. Copy-move forgery is very difficult to detect because the forgery region is part of the same image, and the statistical distribution of the image pixels is similar. Splice detection methods look for changes in the statistical distribution of pixels, differences in the compression level, and various features related to the camera for detecting forgery. There are several methods for detecting copy-move forgeries, such as keypoint-based methods, block methods, segmentation-based methods, texture-based methods, and a combination of keypoint and block methods. Numerous methods, such as camera-based methods, Markov feature-based methods, compressed image-based methods, and transform-based methods, have been introduced by researchers to detect splicing forgery. Detecting a type of forgery is inefficient because it is impossible to identify the type of forgery quickly by looking at the fake image. A few methods have been presented to detect various types of image forgery [4]. With the emergence of machine learning and deep learning, these techniques have been applied to classify fake images and locate affected regions. Deep learning aims to learn high-level abstracts from data using hierarchical architectures. Convolutional neural networks (CNNs) have a great learning capacity due to thin connections and weight sharing and have received much attention in recent years. The U-Net model has become a popular component identification algorithm in recent years due to its precise response, high accuracy, high speed of processing and learning, no need for large data sets for learning, and no need for complex and expensive hardware. This research proposes image segmentation using the modified U-Net deep learning model to classify and extract features. Therefore, in this paper, our contributions include:  
1. We are proposing a new method based on a U-net CNN to improve the fine details of images, which aims to increase the efficiency of image forgery detection. 2. Inspired by the proposed deep learning model for complex medical image segmentation [5], we intend to conduct a similar task to exploit the unique features of U-Net for image enhancement in the current work. 3.