## 4 Numerical Results
### Results and Discussion
#### 4.3.7 Effectiveness of Multiple Secondary Users on Different Social Media Platforms  
To further demonstrate the robustness of our proposed distributed source camera identification method, we conducted a series of experiments using images processed through various social media platforms. Table 5 shows the results of source camera identification on digital images from the VISION dataset, which have been subjected to various post-processing operations on different social media platforms. As shown in Table 5, in the NatFBH and NatWA categories, our method achieved near-perfect accuracy (0.9999), which demonstrates the robustness of our method under different image conditions. However, in the NatFBL category, our method obtained a slightly lower accuracy of 0.8855 compared to 0.9912 for [PERSON] et al. This is due to the fact that different social media platforms may exhibit distinct characteristics in terms of image processing and sharing. For example, images on NatFBL may undergo different compression algorithms, resolutions, or color treatments compared to other platforms. As a result, the method proposed by [PERSON] et al. might be better suited to the specific image characteristics of NatFBL. Nevertheless, the overall average accuracy of our method was 0.9618, which demonstrates the better stability of our method in general. The results in Table 5 highlight the effectiveness of our proposed scheme, even when dealing with images of varying quality that have undergone unknown operations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Dresden** & **ALASKA** & **SOCRatES** & **Forchheim** & **SIHDR** & **VISION** \\ \hline Chen [12] & 0.7558 & 0.6759 & 0.2614 & 0.7669 & 0.4500 & 0.9471 \\ Ours & **0.9822** & **0.9793** & **0.9886** & **0.9925** & **0.9750** & **0.9999** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on original images from the six datasets.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Classifier/Secondary User** & **Dresden** & **ALASKA** & **SOCRatES** & **Forchheim** & **SIHDR** & **VISION** \\ \hline Secondary User1 & 0.9750 & 0.9759 & 0.9659 & 0.9925 & 0.9500 & 0.9999 \\ Secondary User2 & 0.9822 & 0.9724 & 0.9886 & 0.9925 & 0.9250 & 0.9999 \\ Secondary User3 & 0.9804 & 0.9517 & 0.9886 & 0.9925 & 0.9250 & 0.9999 \\ Central Classifier & **0.9822** & **0.9793** & **0.9886** & **0.9925** & **0.9750** & **0.9999** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for each secondary user and central classifier.  
Similarly, Table 6 summarizes the results obtained from experiments conducted on the Forchheim dataset, which includes images processed by a variety of social media platforms. Our method consistently outperformed the approach proposed by [PERSON] et al. in most categories, except for the Facebook category. In the Facebook case, [PERSON] et al.'s method slightly surpassed ours, achieving an accuracy of 0.9925, whereas our method recorded an accuracy of 0.9173. The slightly lower performance of our approach on the Facebook platform was due to similar reasons as those observed for NatFBL in Table 5. However, our method achieved a good accuracy of 0.9999 for images processed by Instagram, Twitter, Telegram, and Whatsapp. Moreover, the overall mean accuracy of our method was 0.9834, surpassing the average accuracy of 0.8947 achieved by [PERSON] et al. The results in Table 6 further highlight the adaptability and robustness of our distributed source camera identification method in real-world scenarios.