# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 3 Proposed Splicing Manipulation Detection System Based on Siamese Neural Network
### Feature Extraction Using a Siamese Neural Network
#### 3.2.2 Siamese Neural Network Training  
Training the Siamese neural network to create specific image features involves data augmentation. Because the amount of data required for training is in most cases larger than the image contained in most available databases, the data augmentation process applied different manipulations to each image block to increase the amount of data and achieve more efficient detection by reducing overfitting and underfitting. This image augmentation technique involves precise learning of features or patterns to detect manipulated areas.  
Data augmentation techniques are strategies used to increase data to improve the model's ability to generalize and recognize patterns. In this case, geometric and image processing techniques were applied, which include the following:  
1. Image rotation (0\({}^{\circ}\)-365\({}^{\circ}\)): precise rotations are applied to images in a range of 0 to 365 degrees with a step of 15\({}^{\circ}\), simulating different orientations of objects in the images with accuracy.
2. Image translation: This distortion moves the image horizontally or vertically to simulate different positions within the frame. For this image manipulation technique, an x translation, a y translation, and an _xy_ translation of 10, 20, and 30 pixels were used.
3. JPEG compression: this image processing distortion technique consists of JPEG compression of images with a quality factor of 90, 70, 50, and 30, which can affect the quality and clarity of visual details.
4. Gaussian filter: the Gaussian filter, with a kernel of 5 \(\times\) 5 and 7 \(\times\) 7, smooths the image, which can help eliminate noise and imperfections.
5. Median filter: a median filter with a kernel of 5 \(\times\) 5 and 7 \(\times\) 7 removes noise from the image while maintaining important edges and details.
6. Gaussian noise: Gaussian noise of 0.009 and 0.09 is added to the image to simulate imaging conditions under adverse conditions.
7. Blur: image blur with a kernel of 5 \(\times\) 5 and 7 \(\times\) 7 softens details and creates a diffuse appearance.
8. Denoising: denoising techniques eliminate unwanted noise from the image, improving visual quality.  
These techniques increase the neural network input data, helping the model learn and recognize patterns in different conditions and scenarios and improving its ability to generalize image features.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Branch 1**} & \multicolumn{3}{c}{**Branch 2**} \\ \hline
**Layer** & & & & **Layer** & & \\ \hline CNN 64 & Neurons = 64 & Stride = 2 & Padding = 1 & Kernel = 3 & CNN 1 & Neurons = 64 & Stride = 2 & Padding = 1 & Kernel = 3 \\ \hline Activation & & & & Activation & & & ReLu & \\ Function & & & & Function & & & \\ \hline Max pooling & & Kernel = 2 & & Max pooling & & Kernel = 2 & \\ \hline CNN 64 & Neurons = 64 & Stride = 2 & Padding = 1 & Kernel = 3 & CNN 1 & Neurons = 64 & Stride = 2 & Padding = 1 & Kernel = 3 \\ \hline Activation & & & & Activation & & & ReLu & \\ Function & & & & Function & & & \\ \hline Max pooling & & Kernel = 2 & & Max pooling & & Kernel = 2 & \\ \hline Fully & & & & Fully & & & \\ connected & & & & connected & & & \\ \hline Activation & & & & Activation & & & ReLu & \\ Function & & & & Function & & & \\ \hline \multicolumn{6}{c}{Training Hyperparameters} \\ \hline Epochs & & & & & & & \\ \hline Learning Rate & & & & & & \\ \hline Momentum & & & & & & \\ \hline Margin & & & & & & \\ \hline Batch Size & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Siamese neural network hyperparameters.