# 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., 2020 [17], introduced a CNN-based image forgery detection and localization method that makes decisions based on the entire image without changing its size. This framework consists of three blocks: piecewise feature extraction, image feature combination with several fusion strategies, and global classification. [PERSON] and [PERSON], 2019 [18], and [PERSON] and [PERSON], 2018 [19], discussed all types of image forgery detection techniques: image link detection, copy-move forgery detection, image resampling detection and image manipulation detection. [PERSON] et al., 2020 [20], introduced a face R-ray method to detect forgery in face images and manipulated boundaries in fake images using CNN HRNet in their framework. [PERSON] et al., 2021 [21], performed experiments using two deep learning models for copy-transfer image forgery: small VGGNet and Mobile Net V2. [PERSON] et al., 2020 [22], investigated all types of forgery (digital watermarks, digital signatures, image linking, image manipulation, and copy-transfer forgery). [PERSON] and [PERSON], 2020 [23], introduced a U-Net composed of dense convolutional and de-convolutional networks. The first network is a deductive sampling method for feature recovery, and the second network is an incremental sampling approach for feature map size recovery. [PERSON] et al., 2018 [24], designed a CNN segmentation approach to find regions in digital images. First, a unified CNN architecture was developed to handle sliding windows with different input scales and colors. Then, high-accuracy CNN training processes were built by sampling the training areas. [PERSON] et al., 2020 [25], used a non-fixed and fixed encoder to build a U-net with a dual encoder (D-Undet). The unfixed encoder captures the fingerprint of the image, which cleans the original and fake regions. In contrast, a fixed encoder provides direction data to facilitate network training and detection. [PERSON] et al., 2018 [26], tested the effectiveness of several image forgery detectors on image-to-image translation, including both ideal settings and even compression modes. Compression is generally used when uploading images to social media sites. [PERSON] et al., 2021 [27], introduced a method based on multi-image splicing using MobileNet V1. [PERSON] and [PERSON], 2019 [28], proposed a framework in which images are fed to a CNN and then processed by several layers for feature extraction and was finally used as a training vector for a recognition model. For feature extraction, ResNet-50 pre-trained deep learning was used. [PERSON] et al., 2020 [29], proposed the attention method to analyze and refine feature maps for recognition tasks. Learned attention maps emphasize functional regions and identify altered regions to improve binary classification (fake face vs. real face). [PERSON] et al., 2019 [30], built a convolutional neural network that uses a multi-task learning strategy to detect and segment manipulated facial images and videos and locates forged regions. Information received from one task is shared with the second task, thus improving the efficiency of both activities. A semi-supervised learning strategy has been used to strengthen the generality of the network. An encoder and a Y-shaped decoder were placed within this network. [PERSON] and [PERSON], 2018 [31], introduced exposing deepfake videos by detecting face-warping artifacts. Deepfake techniques create fixed-size images of the face that must be carefully warped to match the original facial makeup. Due to the dispersion of resolution between the warped face area and surrounding texture, this warping operation produces different side images. As a result, deepfake videos are detected using these side images. [PERSON] et al., 2018 [32], introduced a method for learning image features by training a CNN to detect the 2D rotation applied to the received input image. They used unsupervised representation learning by predicting image rotations.