# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 4 Experiment and Evaluations
### Performance Evaluation
To further demonstrate the effectiveness of our proposed scheme on a quantitative basis, we experimentally obtained the Kappa coefficient of the four algorithms. To clearly show which enhancing scheme performs better, each row represents the Kappa coefficient obtained by different algorithms for the same image size, and the best enhancing scheme is indicated in bold. According to Table 2, the Proposed method always has excellent performance under different size datasets. In recent years, deep learning methods have been applied to image forensics. [PERSON] et al. [14] proposed a method for extracting camera model fingerprints using Siamese neural networks. We call the method \"Cozzolino2019\" and we use it as a baseline to compare the Proposed scheme's performance of SCI. For a fair comparison, training was performed in Cozzolino2019 under strict adherence to the original conditions. The comparison experiments on the Dresden dataset used the 49 camera devices in Table 1. The experimental image sizes were \(512\times 512\), \(256\times 256\), and \(128\times 128\) pixels. In Figure 8, the ROC curves of the Proposed method are significantly higher than the Cozzolino2019 method for different image sizes. The Proposed method's ROC curve is closer to the upper left corner of the coordinate, indicating its better SCI performance. Figure 8a represents the ROC performance of SCI for image sizes of \(512\times 512\) pixels. The Proposed method has an AUC = 0.9917 while the Cozzolino2019 method has an AUC = 0.8319. Figure 8b represents the ROC performance of SCI for image sizes of \(256\times 256\) pixels. The Proposed method has an AUC = 0.9730 while the Cozzolino2019 method has an AUC = 0.7871. Figure 8c represents the ROC performance of SCI for image sizes of \(128\times 128\) pixels. The Proposed method has an AUC = 0.9328 while the Cozzolino2019 method has an AUC = 0.6680. The ex \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **[PERSON] [6]** & **[PERSON] [7]** & **[PERSON] [9]** & **Proposed** \\ \hline \(512\times 512\) & 0.7810 & 0.8586 & 0.9009 & 0.9292 \\ \(256\times 256\) & 0.6191 & 0.7477 & 0.8114 & 0.8411 \\ \(128\times 128\) & 0.4660 & 0.5453 & 0.6472 & 0.6764 \\ \hline \end{tabular}
\end{table} 
Table 2: The Kappa statistic of the four methods. Figure 7: The AUC of the four methods at \(128\times 128\) blocks using 49 camera devices. The [PERSON] method proposed by [PERSON] et al. [6], the [PERSON] method proposed by [PERSON] et al. [7], the [PERSON] method proposed by [PERSON] et al. [9]. perimental results demonstrate that the Proposed method achieves a better performance than the Cozzolino2019 method for source camera identification on the Dresden dataset. To fully demonstrate that the performance of the Proposed method is better than the Cozzolino2019 method in the SCI, we further proceeded to conduct comparative experiments on another large dataset. The VISION dataset [26] consists of camera-native JPG images from various mobile devices. Table 3 presents the basic information of the used cameras, the value in the 'Index' column represents the index number of the camera in the VISION dataset. For each chosen camera, the reference PRNU noise is estimated using 50 flat filed images with simple textures. Then, 150 test images of this camera are selected as the intraclass sample and 1440 test images of the other cameras are selected as the interclass samples. In Figure 9, the performance of SCI is reported on \(128\times 128\) pixels, showing only the ROC curves of the four cameras. According to the comparison of the ROC curve in Figure 9, the Proposed method shows great advantages in terms of the AUC values as the total area under the curve increases significantly on different cameras. This proves that the Proposed method also has satisfactory results on the VISION dataset. Figure 8: The overall ROC curves with two methods for different block sizes. The Cozzolino2019 method proposed by [PERSON] et al. [14].