# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 1 Introduction
### Related Work
#### 1.1.1 Detecting Copy-Move Forgery Using Block Methods  
[PERSON] et al., 2016 [6], reviewed the latest techniques in the field of copy-move forgery detection. They described copy-move forgery detection using block and keypoint methods. [PERSON] et al., 2017 [7], introduced a technique based on image segmentation and an algorithm called swarm intelligence (SI). This technique divided the image into small blocks without overlapping (common parts). The SI algorithm was implemented to find the best possible parameters in each layer. With the scale-invariant feature transform (SIFT), these parameters were used to identify each layer. This method had a high false-positive rate. [PERSON] et al., 2019 [8], proposed a method in which the image blocks are transformed into discrete wavelet transforms and used to extract features from a new fractional texture descriptor based on Machado fractional entropy. Deep learning-based techniques have outperformed advanced machine learning-based methods in many computer vision applications. In detecting the forgery of digital images, [PERSON] et al., 2017 [9], introduced two methods to detect image forgery. In the initial technique, an end-to-end system was presented for detection. Then, localization in manipulated digital images was performed based on Radon transformation and deep learning methods. The Radon transform is an integral transformation with a practical inverse application in rebuilding images from medical CT scans. Additionally, a method has been created to utilize the Radon transform in constructing a map of the polar regions of a planet by a spacecraft orbiting around its poles. The feature resampling combination was used in the second technique, and these features were obtained based on maps. In order to classify, the LSTM model was used to find manipulated areas. [PERSON] et al., 2016 [10], introduced a two-stage deep learning method for feature learning. In the first stage, stack automatic encoders were used, by which the model learns the characteristics of each piece of the image. In the second step, the texture information of the whole image is integrated. [PERSON] et al., 2017 [11], introduced a technique based on the block method. The processing unit of each block is a powerful CNN. This method detected splicing forgery, and its effectiveness was observed in JPEG compression. [PERSON] and [PERSON], 2017 [12], introduced an image manipulation detection algorithm using a deep CNN model. This neural network consists of four steps: a high-pass filter, two convolutional layers, two fully connected layers, and an output layer. In the experiments, the images were resized to 256 \(\times\) 256 dimensions using the median filter method, Gaussian filtering, and collective white Gaussian noise. Finding an ink mismatch is an essential step in detecting image forgery. [PERSON] et al., 2018 [13], introduced a deep learning method to detect ink gaps in hyperspectral document images. These researchers extracted the spectral responses from the ink pixels of the hyperspectral document image, converted them to an image format suitable for CNNs, and fed them to a CNN for classification. The proposed technique successfully recognized different types of ink in super-sized document images for forgery detection. [PERSON] et al., 2017 [14], introduced an effective copy-transfer manipulation detection technique based on a convolutional kernel network (CKN), which is a combination of matrix calculations and convolutional operations. [PERSON] et al., 2018 [15], introduced a weakly supervised domain adaptation method for distinguishing between real and fake images. These researchers trained the auto-anchor-based method on the source domain and separated fake and authentic images in the hidden space. [PERSON] and [PERSON], 2020 [16], introduced a single-class classification model based on a one-class variational auto-encoder to detect fake images of human faces.