# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 2 Materials and Methods
### Training Strategy  
Figures 7 and 8 illustrate the generation of tampered images within the CASIA V1.0 dataset. The images in the dataset were tampered with using a number of tampering approaches, but only splicing was taken into account in the CASIA database version 1.0. Therefore, the altered images within the dataset are known as spliced images. Figure 8 demonstrates another instance of image splicing in the V1.0 dataset. Panel (a) of the figure depicts an authentic image of a landscape, while panel (b) depicts a composite image created by copying and pasting a portion of the landscape onto a different background. This technique is used to create altered images that are visually difficult to detect, as they may appear authentic at first glance.  
Figure 6: An example of database images [45].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **No. of Images** \\ \hline JPEG Format & 2064 \\ \hline TIFF Format & 3059 \\ \hline Manipulation without pre-processing & 1843 \\ \hline Manipulation without post-processing (blurring) & 4144 \\ \hline Source of & Same Image & 3274 \\ Tampered & Different Images & 1849 \\ \hline Rotation & 568 \\ \cline{2-3} Manipulation & Resize & 1648 \\ \cline{2-3} Manipulation & Distortion & 196 \\ \cline{2-3} with & Rotation and Resize & 532 \\ \cline{2-3} pre-processing & Resize and Distortion & 211 \\ \cline{2-3}  & Rotation and Distortion & 42 \\ \cline{2-3}  & Rotation, Distortion & 83 \\ \cline{2-3}  & and Resize & \\ \hline Manipulation & Blurring along spliced edges & 848 \\ \cline{2-3}  & Blurring on other regions & 131 \\ \hline \multirow{3}{*}{Size of Tampered  Region} & Small & 3358 \\ \cline{2-3}  & Medium & 819 \\ \cline{2-3}  & Large & 946 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some statistical information about the spliced images in CAISA ITDE V2.  
CASIA v2.0 is a supplement to CASIA v1.0, containing additional tampered images. This dataset includes images with more complex tampering techniques, such as local contrast enhancement and color modification. The images in this dataset are also classified into the same eight content categories as CASIA v1.0.  
The use of these datasets allows for a comprehensive evaluation of the proposed method across a wide range of tampering techniques and content categories. 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.  
Figure 9 shows how tampered images were generated in CASIA V2.0. In this example, a tampered image was created by splicing two different regions from different images. One region was taken from an image of a statue, while the other was taken from an image of a sky. The spliced region was then inserted into a new background image, resulting in a tampered image.  
The most significant difference between the V1.0 and V2.0 tampered sets is the blurring operation usage after generating a spliced image. The database also includes several manually tampered images to make it more comprehensive. V2.0 considers the following criteria while generating tampered images:  
* Illumination changes to spliced regions;
* Splicing with blurring;
* Copy-move;
* Text insertion;
* Image retouching.  
Figure 10 illustrates an example of generating tampered photos in CASIA V2.0. In this example, (a) displays the legitimate photographs, and (b) displays the tampered images that match them.  
Figure 8: This is an example of how to generate tampered photos using CASIA V1.0: (**a**) authentic photos; (**b**) spliced images [47].  
Figure 7: An illustration of producing manipulated images within the CASIA V1.0 dataset [46].