# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 3 Proposed Splicing Manipulation Detection System Based on Siamese Neural Network
### Image Preprocessing  
In the preprocessing stage, the image is resized to 512 \(\times\) 512 and divided into non-overlapping 32 \(\times\) 32 blocks. These blocks are used to learn specific features from each image region during the Siamese neural network training, making it easier to identify manipulated areas. Image block segmentation is used because most image splicing manipulations modify specific image regions, so by dividing the image into smaller blocks, a more accurate representation of each area of the image can be generated, and each block is considered an independent region. The blocks contain significant information and simplify the Siamese network's learning task for feature comparison. Furthermore, this strategy facilitates network training and improves computational efficiency. A block size of 32 \(\times\) 32 pixels allows the Siamese network to learn the specific local features of each region; this is crucial to identifying visual and textural patterns indicative of image splicing manipulations.  
Image preprocessing divides the image into blocks to be processed by the neural networks. Image block segmentation provides a better analysis of the regions, increasing the efficiency of the proposed method. The following equation expresses the number of blocks generated in this process.  
\[Number\ of\ blocks=\bigg{(}\frac{M}{m_{block}}\bigg{)}\bigg{(}\frac{N}{n_{block}} \bigg{)} \tag{1}\]  
where \(M\) is the image width, \(N\) is the image height, and \(m_{block}\) and \(n_{block}\) are the width and height of the block, which are 32 in the proposed schemes.  
The feature extraction process leverages the Siamese neural network architecture depicted in Figure 2. Image features are extracted from the convolutional layers of this network, generating a comprehensive feature matrix as the output. This process analyzes each region of an image separately, generating semantic descriptions of the image.  
The convolutional layers detect local features such as edges, textures, and simple patterns in early layers, while later layers detect more complex and abstract features. The  
Figure 2: Siamese neural network architecture.  
feature extraction process generates abstract representations containing essential information for identifying image manipulations. Convolutional layers reduce input dimensionality by applying filters and grouping important features, which reduces model complexity and helps extract more compact representations. The feature matrix construction with the extracted features from these layers provides precise tampering detection through dimensionality reduction. The feature matrix encodes patterns from the original and manipulated images, enhancing the model's generalization to detect splicing manipulations. Therefore, the Siamese neural network uses the extracted features to differentiate between authentic and manipulated regions within an image for splicing detection.  
The output of the neural network, which is the Block Feature Matrix, has dimensions of 2 by the number of blocks, as given by (1).