# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 4 Experimental Results
### Performance Evaluation of the Proposed Algorithm  
The metrics used to evaluate the efficiency of the proposed system are essential to measuring its performance. First, accuracy, given by Equation (5), determines whether the pixels in the manipulated area correctly correspond to the manipulated area. \[acc=\frac{Correct\ pixels\ prediction}{Total\ predictions} \tag{5}\]  
Additionally, the precision evaluates the number of pixels correctly predicted as part of the tampered area. This metric provides specific information about the system's ability to predict manipulated regions, and it is given in Equation (6). \[precision=\frac{Tp}{Tp+Fp} \tag{6}\]  
True positives (_TP_) are the pixels correctly classified as part of the region of interest, and false positives (_FP_) are the pixels incorrectly classified as part of the region of interest. On the other hand, _recall_ measures the algorithm's effectiveness in appropriately detecting the pixels that belong to the tampered area. This metric highlights the system's ability to identify manipulated areas, as given in Equation (7). \[recall=\frac{Tp}{Tp+Fn} \tag{7}\]  
The _F1_ score metric provides a value that evaluates the overall detection capacity, combining both precision and _recall_. It is useful for obtaining a balanced view of the system's performance, as given in Equation (8). \[F1=\frac{2(Precision\ x\ Recall)}{Precision+Recall} \tag{8}\]  
Finally, the mean-squared error quantifies the error obtained in the detection of the manipulated areas. It provides a quantitative measure of the system's performance, as given in Equation (9). \[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(I^{\prime}(x,y)-I^{\prime}(x,y)\right)^{2} \tag{9}\]  
These metrics offer a complete and detailed evaluation of the proposed system in terms of its ability to detect and classify image-splicing-manipulated areas. Figure 5 illustrates the results of our image splicing detection method across four distinct databases: Realistic Tampering, Own, CASIA V2, MICC-F220, and Columbia. Notably, our method exhibits exceptional performance within the Realistic Tampering database, showcasing its ability to identify altered regions within images from this dataset accurately. In our proprietary database (Own), our method demonstrates high efficiency, reaffirming its effectiveness in detecting image splicing manipulations. However, within the CASIA V2 database, our method's efficiency appears comparatively lower. This observation may stem from the unique characteristics of the CASIA database, which includes images subjected to splicing manipulations. For the evaluation of the CASIA database, the original image has to be converted to *.tif format since the manipulated image is in this format. Despite this, experimental tests reveal the efficacy of our proposed technique against splicing distortions within the CASIA V2 image dataset. Furthermore, the results obtained from the experiments conducted on the Columbia database demonstrate our method's efficient detection capabilities. This outcome underscores the versatility and robustness of our algorithm in identifying image splicing manipulations across different datasets. Figure 5(a) shows the accuracy of the proposed algorithm, obtaining an accuracy mean value of 0.9843 for splicing detection in the three image databases. This result indicates that the algorithm has a high level of ability to correctly classify manipulated or nonmanipulated regions. The precision is illustrated in Figure 5(b); this graphic represents the algorithm's ability to identify the image's splicing regions efficiently. The obtained precision mean value is 0.9757. This value underscores the algorithm's robustness for the detection of manipulated regions. Figure 5(c) depicts the algorithm's recall, which is 0.9540, indicating that the proposed algorithm has a high capacity to detect manipulated regions, thus minimizing false negatives. The \(F1\) score is shown in Figure 5(d), achieving a mean value of 0.9635.