# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 5 Discussion  
The analysis of the results determines that the proposed method based on Siamese neural networks is an efficient tool for detecting splicing manipulations in digital images. The evaluation results show that the proposed scheme, in which the image under analysis is resized to 512 \(\times\) 512 and segmented into non-overlapped blocks of 32 \(\times\) 32 pixels, which are then used to train the Siamese neural network, is an efficient technique for detecting image manipulations by comparing blocks and determining their similarity. This allows for the accurate and efficient detection of manipulated areas, improving the effectiveness of the proposed method.  
Furthermore, the Siamese neural network extracts the inherent and invariant features of each image block and learns patterns related to each image region, increasing its efficiency in detecting manipulated areas. For this reason, this technique detects manipulated regions despite applying geometric distortions to the duplicated region. The application of data augmentation during the training of the Siamese neural network, through geometric distortions and image processing, increases the system's robustness by generating image representations efficiently, avoiding underfitting and overfitting. The detection of the manipulated region using the K-means algorithm helps determine the similarity between image blocks to detect this region efficiently. The results obtained in the experimental tests demonstrated the effectiveness of the proposed algorithm in detecting splicing manipulations in images, providing higher accuracy than other previously proposed schemes. The high performance of the algorithm in detecting manipulated areas in the Realistic Tampering image database and our database is highlighted, as is its ability to adapt to different types of manipulations, as in the CASIA V2 and Columbia databases. It is important to mention that our system can detect altered areas by using the splicing technique.  
It is worth mentioning that adjusting the blocks' size during preprocessing impacts the neural network's training time and the method's efficiency. An increase in block size results in a reduction in training time, but it also decreases the efficiency of the proposed method. On the other hand, reducing the size of the blocks increases the processing time but improves the efficiency in detecting splicing manipulations. Therefore, a balanced relationship must be established between the computational cost, processing time, and efficiency of the proposed system. This relationship will depend on the system's specific features, where the proposed methodology is implemented, and the user's requirements. Table 3 shows that most of the neural networks used as the backbone in the algorithms compared with the proposed method have more trainable parameters, which increases the training time. This effect is reflected in the testing times taken by the algorithms. On the other hand, algorithms that use neural models with a more significant number of  
Figure 7: Processing time comparison.  
parameters can, in some cases, show increased efficiency; however, this entails a higher computational cost. An example of this is the method of [PERSON] et al. [26], which presents a slightly higher efficiency (98.79%) compared to the proposed method (98.6%) but with a significantly longer training time (58.05 s versus 5.25 s), thus demonstrating a higher computational cost.  
On the other hand, the method proposed by [PERSON] and [PERSON] [24] uses the MobileNet neural network, which contains a smaller number of trainable parameters and a shorter training time (2.55 s), although it obtains a lower efficiency (93%). The technique developed in this study allows for obtaining an algorithm with high efficiency, providing a favorable balance between efficiency and computational cost.