# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 2 Methodology
### MCIFFN Structure
#### 2.1.3 Multiscale Fusion Analysis  
The content and scenes of photos are rich and diverse. There are few pairs of photos that are identical. The same manufacturer or the same camera model might not capture the same or similar content. The scene taken by each camera is random. Therefore, it is impossible to track the camera through the content of the image. By contrast, the randomness and diversity of the image content are the largest interference factors of effective feature extraction. The traditional CNN is designed to solve the task of computer vision. The focus of the network is on the image content. Therefore, a preprocessing module should be added before the CNN to suppress features related to the image content. The preprocessing module is similar to a spatial filter \(G\), which can suppress the content feature of image \(I\) and enlarge the image noise feature \(N\). \[N=I*G \tag{1}\]  
The method presented in [39] added a constraint convolution layer to the front of the CNN to suppress image content and adaptively learn image-tampering features. The method presented in [40] used an SRM filter to extract local noise features and detected tampering traces through noise features. The method presented in [41] embedded a Laplacian filter into the first layer to improve the signal-to-noise ratio introduced by the recapture operation. The method presented in [35] designed a convolutional neural network similar to AlexNet for image source detection and preprocessed it with a local binary pattern (LBP). Although the preprocessing method above can suppress the image noise to some extent, because the filter function in the preprocessing layer is too single, it can only remove part of the image content, and improvement in network performance is limited. Figure 2 shows the proposed multiscale feature fusion network architecture. MCIFFN is composed of three stream networks. There is a preprocessing module at the entrance of the first two stream networks to introduce domain knowledge. The third stream network does not preprocess to save image color information. Due to the randomness of the scene, the image has various scale feature information. The image content can be divided into \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer Number** & **Layer Type** & **Kernel Size** & **Filters** & **Stride** & **Pad** \\ \hline
1 & Convolution & \(3\times 3\) & 64 & 1 & 1 \\
2 & Max pooling & \(2\times 2\) & - & 1 & 0 \\
3 & Convolution & \(3\times 3\) & 32 & 1 & 1 \\
4 & Max pooling & \(2\times 2\) & - & 2 & 0 \\
5 & Convolution & \(3\times 3\) & 32 & 1 & 1 \\
6 & Max pooling & \(3\times 3\) & - & 2 & 0 \\
7 & Convolution & \(3\times 3\) & 64 & 1 & 1 \\
8 & Max pooling & \(2\times 2\) & - & 2 & 0 \\
9 & Convolution & \(3\times 3\) & 128 & 1 & 1 \\
10 & Max pooling & \(2\times 2\) & - & 2 & 0 \\
11 & fc & - & 128 & - & - \\
12 & ReLu & - & - & - & - \\
13 & fc & - & 23 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network Parameters of SE-SCINet. smoothing, saturation, and others. The frequency be divided into high-frequency and low-frequency information. The degree of information density can be divided into sparse information and dense information. A single-scale filter cannot effectively suppress the multiclass content information in the image. Therefore, we add two kinds of receptive field scale adaptive filters to each preprocessing module: a \(3\times 3\) filter is mainly used to remove the dense information in the image, and a \(5\times 5\) filter is mainly used to remove the sparse information in the image. In previous preprocessing schemes such as the Laplacian filter and SRM filter, the filter parameters are manually set to suppress specific types of image content. Image forensics tasks have a variety of key features, such as CFA, SPN, PRNU, and other complex features. Although the filter with fixed parameters can suppress the interference features (image content-related features), it may also destroy some key features.