# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 2 Materials and Methods
### Datasets  
The Chinese Academy of Sciences Institute of Automation (CASIA) image tempering detection evaluation dataset [44] has been used in our study to test the performance of the U-net model processed via our proposed methodology. This dataset contains a wide variety of images, including linking forgery and copy and transfer forgery, which makes it an excellent choice for evaluating the effectiveness of our proposed approach. The images in this dataset are classified into various categories based on their content, including nature, animals, architecture, people, plants, objects, scenes, and textured images. The use of the CASIA dataset provides a comprehensive and challenging evaluation of our proposed method and allows us to determine its effectiveness in detecting forgeries across a range of different image types and content. This enables us to determine the method's effectiveness in detecting forgeries across different image categories. This dataset is widely considered to be a more challenging and realistic dataset for manipulation detection, making it an ideal choice for evaluating the effectiveness of our method in real-world scenarios. Table 1 provides an overview of the CASIA v1.0 and CASIA v2.0 datasets that were used in the experiments conducted for the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Image Type** & **Image Size** & **Authentic** & **Spliced** \\ \hline CASIA v1.0 & jpg & \(384\times 256\) & \(800\) & \(921\) \\ \hline CASIA v2.0 & jpg tif bmp & \(\begin{array}{c}240\times 160\\ 900\times 600\end{array}\) & \(7491\) & \(5123\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of CASIA v1.0 and CASIA v2.0 [44]. Figure 5: The process of using the grasshopper algorithm in the proposed method. Table 2 provides statistical data on the types of spliced images that were created and how they were arranged in the CASIA V1.0 dataset. The table is organized into several categories, including the number of images in JPEG format, the types of manipulations used, the source of the tampered regions, and the shape of the tampered regions. Table 2 is helpful in understanding the characteristics of the spliced images in the CASIA V1.0 dataset and the way they were manipulated using different techniques. The inclusion of more complex tampering techniques in CASIA v2.0 provides a more challenging and realistic evaluation of the proposed method's effectiveness in detecting forgeries in real-world scenarios. Overall, the use of these datasets provides a rigorous and comprehensive evaluation of the proposed method, enabling us to determine its effectiveness in detecting forgeries across a range of different tampering techniques and content categories. V2.0 considers the following criteria while generating tampered images:  
* Illumination changes to spliced regions;
* Splicing with blurring;
* Copy-move;
* Text insertion;
* Image retouching. Table 3 provides statistical information about the spliced images in the CAISA v2.0 database. The table shows the number of images in various categories, such as the format of the images, the type of manipulations applied to the images, the source of the tampered regions, the size of the tampered regions, and so on. Furthermore, the table provides information on the type of manipulations applied to the images, such as rotation, resizing, and distortion, either alone or in combination. It also displays the number of images with post-processing blurring, either along spliced edges or other regions.