# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 3 Experiment and Evaluation
### Performance of MCIFFN  
In this experiment, we will verify the rationality of the MCIFFN architecture from the filter size, network structure, and other aspects. We will select 23 camera models from the  
in the MCIFFN are \(5\times 5\), MCIFFN-1-2 represents the converged network of branches 1 and 2 of the MCIFFN network and MCIFFN-NoRes is the network that removes the identity mapping between the \(3\times 3\) and \(5\times 5\) filters of the MCIFFN network.  
The test accuracies of MCIFFN-1, MCIFFN-2, and MCIFFN-3 are lower than that of MCIFFN, which proves that the multibranch fusion scheme can effectively combine a variety of key features and that the network can learn more abundant noise features. From the test results of MCIFFN-F3 and MCIFFN-F5, we can see that there are a variety of image content-related interference features in the image, and the combination of multiscale filters can better suppress the image content. From the test results of MCIFFN-NoRes, it can be seen that the network preprocessing module adds a direct channel to fuse the noise extracted by the two size filters, which can effectively extract a variety of key forensic information.  
From the test results of MCIFFN-1-2, it can be seen that although the image content interferes with the extraction of key features, there are still some features related to the source camera in the color-related information. Therefore, our design scheme still retains information flow without preprocessing. MCIFFN-NoRes test results show that adding a direct channel between the two filters can effectively suppress different types of image content information. From the test time of a single image, although the multibranch fusion scheme is more time-consuming, the time difference is not large. From the comprehensive test accuracy and the test time of a single image, the MCIFFN architecture is the best scheme.  
Table 2 shows the performance test results of MCIFFN embedded in traditional networks. MCIFFN-AlexNet and MCIFFN-ResNet18 are MCIFFN networks whose CNN is replaced by AlexNet and ResNet18. The test results of MCIFFN-AlexNet and MCIFFN-ResNet18 show that the MCIFFN framework is also suitable for traditional feature extraction networks. The test results of AlexNet and ResNet18 are much lower than those of MCIFFN-AlexNet and MCIFFN-ResNet18, which indicates that the traditional shallow network is not suitable for image forensics tasks directly, and an image content suppression module needs to be added to achieve better results.  
To test the performance of the MCIFFN network, we compare the MCIFFN with other existing preprocessing methods, and the results are shown in Table 3. The table records the detection accuracy of each algorithm and the time required to test a single \(64\times 64\) pixels image patch. This time is obtained by averaging 160,455 test images in the test set. The classification accuracy of LBP-CNN, Laplacian CNN, and HP-CNN is far lower than that of MCIFFN and CAF-CNN. Although CAF-CNN and MCIFFN are close in classification accuracy, their network complexity and the network test time of a single image are far greater than those of MCIFFN. In summary, the MCIFFN has the best classification performance.  
To show the classification performance of the MCIFFN more clearly, we group the classification accuracy of each class of cameras in the form of a confusion matrix, and the results are shown in Figure 6. The Figure shows the brand and model information of all cameras involved in this experiment and their classification accuracy. The test result also shows that it is more difficult to distinguish between cameras with the same brand whose feature similarity is higher than that of cameras with different brands, but overall, the classification accuracy can meet the needs of industrialization.