# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 4 Conclusions  
Nowadays, due to the importance and attractiveness of social media and the increasing expansion of image editing and processing software tools, as well as the significant \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \begin{tabular}{c} **Train/Valid** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Highest** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Highest** \\ **AUC** \\ \end{tabular} \\ \hline D-Net & 9:1 & 87.64\% & 0.928 \\ \hline CNN–HPF & 9:1 & 93.73\% & 0.978 \\ \hline RRU-Net & 9:1 & 79.58\% & 0.882 \\ \hline Mask R-CNN + Sobel filter & 9:1 & 81.28\% & 0.928 \\ \hline CNN based on pre-trained Alex Net model & 9:1 & 95.40\% & 1 \\ \hline Deep Neural Architecture-Buster Net. & 9:1 & 84.37\% & 0.938 \\ \hline D-Net & 7:3 & 95.50\% & 0.968 \\ \hline CNN–HPF & 7:3 & 96.58\% & 0.951 \\ \hline RRU-Net & 8:2 & 88.95\% & 0.922 \\ \hline Mask R-CNN + Sobel filter & 8:2 & 93.50\% & 0.982 \\ \hline CNN based on pre-trained Alex Net model & 8:2 & 95.70\% & 0.965 \\ \hline Deep Neural Architecture-Buster Net. & 8:2 & 96.51\% & 0.946 \\ \hline U-Net with GOA & 8:2 & 100.00\% & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Result analysis of detecting image forgery on CASIA dataset. reduction in the costs of using these tools, forging and manipulating digital images is not only as simple as possible, it is acceptable. It has become the most common type of manipulation and forgery, from the most straightforward and most accessible image editing software used by regular users to the latest methods based on artificial intelligence, such as deep forgery technology that uses the architecture of cross-generative networks. The field of document forgery has also been equipped with deep learning tools. This progress in the tools and intelligence of methods of forgery detection and the authentication of images has continuously faced new challenges and problems. As mentioned earlier, copy-transfer forgery is the most common and one of the most accessible types of image manipulation. With this method, one or more areas of the image are copied, and after some changes, they are placed in another section of the same image. Counterfeiters perform many pre-processing or post-processing attacks, such as rotation, filtering, compression, rescaling, etc., on these areas to make the act of detecting forgery even more difficult. Therefore, the detection method should resist such changes and geometric transformations. Typically, to detect this type of forgery, small blocks of the image are compared to each other. For this purpose, several methods have been proposed in this field, which, despite the diverse quantity, follow two general approaches: (1) block-based methods that examine all the blocks of the image and (2) methods based on key points, focusing more on the information-rich areas with unique characteristics. This research introduced a solution based on deep learning to detect a forgery in digital images. The method of designing and implementing the network architecture and the details of the layers of convolutional neural network architecture used were explained. This paper introduced a modified model based on U-NET architecture for correct image segmentation. The proposed model adds complementary convolutional layers in the encoder and decoder pipelines and improves the convolution module to establish a connection between the encoder and decoder, which is a great advantage for extracting features from the last layer of the encoder path and performing segmentation. Adding more sets of weights to a U-NET improves its performance. The results obtained from the proposed model have proved the proposed model's efficiency in segmenting undetectable and interconnected areas, and the performance evaluation criteria have reached a better state than the basic U-NET method. Also, the AUC and accuracy were obtained as 0.99 and 100%, respectively. Hence, the proposed U-NET method has improved image forgery detection. Conceptualization, N.G. and K.P. ; methodology, N.G. and K.P.