# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 4 Experimental Results
### Performance Comparison of the Proposed Algorithm  
Table 2 shows a performance comparison between the proposed algorithm and other previously reported splicing tampering detection algorithms. The obtained results show that the proposed algorithm stands out for its high performance and capacity to generalize characteristics through using Siamese neural networks, which increases its precision and effectiveness in detecting splicing manipulations in images. As we can see in Table 2, the accuracy of the proposed method is 98.6%, which is quite similar to the results reported by [PERSON] et al. using the AlexNet network, although in this case, the system was evaluated using only the database CASIA V1. Compared with other methods, the proposed algorithm achieves higher performance. This indicates that the proposed method accurately and efficiently identifies manipulated regions in images, efficiently generalizing the image's features. The performance evaluation using different databases, including some public and our databases, indicates that the proposed model is robust and can adapt to different scenarios and image manipulations, providing higher accuracy and precision than other previously proposed schemes. The Siamese neural networks generate image representations to identify patterns for efficient image manipulation detection. In addition, combining Siamese neural networks with the K-means algorithm helps identify the original region duplicated in the image. Figure 5: _Cont._  
Figure 5: Splicing detection using the proposed systems with different databases. The proposed method based on Siamese neural networks outperforms other techniques based on pre-trained neural network models as the backbone, such as VGG19, DenseNet, ResNet, MobileNet, and GoogLeNet. The proposed Siamese neural network is designed to compare manipulated and original images to determine their similarity, making it ideal for detecting splicing manipulations in images by accurately identifying manipulated patterns. Additionally, these networks typically have a simplified architecture compared to pre-trained neural networks that are larger models, reducing computational complexity by having fewer trainable parameters. By directly comparing images, Siamese networks can learn more robust representations, improving tamper detection by focusing on the differences between regions. Finally, by combining Siamese networks with the K-means model, we can further improve the performance of the proposed method based on Siamese neural networks. Figure 6: Evaluation performance of proposed algorithm: (**a**) accuracy, (**b**) precision, (**c**) _recall_, (**d**) _F1_ score, and (**e**) mean-squared error. technique, the efficiency of tamper detection is improved by more accurately identifying duplicated or manipulated areas in images. The success of the proposed method is due to its specific design, lightweight architecture, and ability to learn robust representations for the accurate detection of image manipulations. It should be noted that we used five metrics to evaluate the system, while most of the previously proposed algorithms only use between one and three metrics. The use of multiple metrics allows us to gain a more complete perspective on different aspects of performance, which can provide a deeper understanding of its effectiveness. Figure 7 compares the processing training time of the proposed Siamese neural network with several established neural models: VGG19, ResNet18, ResNet50, DenseNet121, AlexNet, GoogLeNet, and MobileNet. These models are the basis of most algorithms used for splicing tamper detection. Additionally, Table 3 depicts the number of trainable parameters for each neural network. These results show that the proposed network requires a lower processing time with a lower computational cost, which allows for its more straightforward implementation and potentially faster results, making it an attractive option for splicing tamper detection tasks.