## 5 Discussion  
In this paper, we primarily investigated how to enhance the reliability and accuracy of source camera identification techniques, and validated our scheme on several well-established image datasets. However, when considering the future expansion of the framework's application, several key issues still need further exploration.  
First, regarding dataset diversity, the current evaluation was primarily based on certain widely used classic image datasets. However, in real-world applications, we may encounter images with extreme distortions, such as compression artifacts, low lighting, or adversarial modifications. Therefore, future research should focus on exploring these special cases, to more comprehensively assess the robustness of the framework. Furthermore, images from rare or outdated camera models are typically excluded from mainstream datasets, which may lead to biases in the framework towards certain camera types or brands, potentially causing systemic discrepancies and affecting the fairness of the identification process. To address the above issues, future studies should consider creating synthetic datasets or incorporating images from underrepresented regions and manufacturers, thereby improving the framework's generalizability and practicality.  
Second, the potential enhancement of source camera identification performance through image cropping could be considered. Current research primarily relies on identifying the source camera of complete digital images, which may not fully leverage the feature information from local regions within the image. More importantly, local regions of the image may be forged or tampered with, affecting the overall authenticity of the image. Therefore, future research could explore various image cropping strategies, considering dividing the image into multiple local regions for independent identification, and then combining the results from each cropped region to infer the source of the entire image. This approach could not only help better identify the integrity and authenticity of the image,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Facebook** & **Instagram** & **Twitter** & **Telegram** & **Whatsapp** & **Mean** \\ \hline Chen [12] & **0.9925** & 0.9474 & 0.7744 & 0.9474 & 0.8120 & 0.8947 \\ Ours & 0.9173 & **0.9999** & **0.9999** & **0.9999** & **0.9999** & **0.9834** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results on Forchheim social media platform based images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **NatFBH** & **NatFBL** & **NatWA** & **Mean** \\ \hline [PERSON] [12] & 0.8282 & **0.9912** & 0.8502 & 0.8899 \\ Ours & **0.9999** & 0.8855 & **0.9999** & **0.9618** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results on Vision social media platform based images.  
but also effectively enhance the robustness of the framework, preventing misjudgments caused by local tampering or distortion.  
Third, ethical issues need to be given high priority. For instance, malicious actors could misuse this technology for unauthorized surveillance or to falsely attribute images in legal cases, leading to privacy violations or wrongful accusations. Therefore, future work should delve into privacy-preserving strategies to ensure that the framework complies with relevant privacy protection regulations (such as GDPR or other regional laws) to avoid potential legal and social risks, especially in sensitive application scenarios.  
Overall, future research should focus on expanding the framework's applicability, enhancing dataset diversity, exploring reasonable image cropping strategies, and thoroughly considering the ethical and legal implications, ensuring that source camera identification technology is comprehensively safeguarded in terms of fairness, privacy protection, and practical utility.