# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 4 Experiment and Evaluations
### Performance Evaluation
Take the false positive rate \(FP=0.01\) for example, \(L=21\), 31, 41, and 51 achieve true positive \(TP=0.690\), 0.689, 0.691, 0.685. According to the results of ablation experiments, the values of \(K\) and \(L\) negligible affect the performance of the source camera identification. Therefore, we set \(K=5\), \(L=41\), and \(S=9\) in all experiments to extract scene detail noise in this paper. In the implementation of extracting scene detail, scene detail is actually extracted from the complete \(K\times K\) block centred on the given pixel. The final scene detail pixel value is the average of all the estimates obtained by all local blocks containing the pixel. This strategy is used in [25] too. From the perspective of the overall ROC curve, we present in detail the performance of the enhanced reference PRNU noise scheme. For the convenience of representation, we call the method in [6] \"Lukas2006\", the method in [7] \"Chen2008\", the method in [9] \"Rao2017\", and the proposed scheme \"Proposed\". To demonstrate the fairness of the comparison, all methods use the Mihack filter with the parameter \(\sigma^{2}\) equal to 9 to extract the PRNU noise. All methods use PCE to examine the similarity between the reference PRNU noise and the examined image. In addition, the images used in our experiments are colour images cropped from full-size colour images, and the experimental image sizes are \(512\times 512\), \(256\times 256\), and \(128\times 128\) pixels. Figure 5 shows the ROC curves for the [PERSON], Chen2008, Rao2017, and Proposed methods. For the different enhancement schemes in Figure 5, the line of the Proposed method always lies above the other lines, with Rao2017 in second place, followed by the Chen2008, and Lukas2006 methods. Compared with Rao2017, our proposed scheme effectively improves the quality of the reference PRNU noise and improves the performance of the SCI. Although the accuracy of the Proposed scheme decreases as the pixel block size decreases, it still has an advantage in small and medium image blocks. However, Figure 5c shows that the Proposed scheme is clearly advantageous, especially for small image blocks. The reason for this is that it is difficult to estimate the PRNU from small image block, so PRNU is easily polluted with image details. Other enhancement methods ignore the local statistics of interfering noise, affecting the suppression effect of interference noise. Similarly, the area under curve (AUC) values in Figure 6 proves that our scheme is more effective in removing the interference noise from the reference PRNU noise. For \(512\times 512\) image blocks, the accuracy of the Proposed method reaches 0.983. Figure 4: Effect of the \(L\) value on the SCI performance for a block size of \(128\times 128\) in ablation experiments. Figure 3: Effect of the \(K\) value on the SCI performance for a block size of \(128\times 128\) in ablation experiments. We calculated the accuracy of the four methods on \(128\times 128\) blocks using 49 camera devices, as shown in Figure 7. It can be seen that the Proposed scheme is more accurate than the [PERSON], Chen2008 and Rao2017 methods. In Figure 7, the median accuracies obtained by the Rao2017 and Proposed schemes are close, but when comparing the boxplots  
Figure 5: The overall ROC curves for four methods on different block sizes. The [PERSON] method proposed by [PERSON] et al. [6], the Chen2008 method proposed by [PERSON] et al. [7], the Rao2017 method proposed by [PERSON] et al. [9]. Figure 6: The AUC of the four methods for different block sizes. The [PERSON] method proposed by [PERSON] et al. [6], the Chen2008 method proposed by [PERSON] et al. [7], the Rao2017 method proposed by [PERSON] et al. [9]. of the Rao2017 and Proposed schemes, we can see that the third quartile (Q3) of the Proposed schemes is greater than the Rao2017. The proportion of accuracy greater than the median is also higher in the Proposed compared to Rao2017 scheme. This also proves that our solution has a better ROC performance on small image blocks.