## 5 Conclusions  
In this paper, the impact of language-specific characteristics on forgery detection accuracy has been analyzed by investigating the differences in the forgery detection performance between English and Korean document images. The experimental results revealed that the visual complexity of forged documents varied by languages, and the outcomes of the digital document forgery detection models differed depending on which language's dataset was used. These findings highlighted the limitations of relying solely on single-language datasets and emphasized the need to develop multilingual datasets and model architectures optimized for various languages. Furthermore, this study employed the SIFT and GLCM algorithms to analyze the differences between original and forged documents in English and Korean, and the results confirmed that the forgery characteristics of English and Korean documents were distinct. In addition, the results showed that detection accuracy improved when the training and target language matched, reinforcing the necessity of language-specific datasets for effective forgery detection. Given that most available datasets for digital document forgery detection are primarily based on English and Chinese, there is a growing need to develop datasets in other languages, including Korean, to enhance detection accuracy in diverse linguistic environments. However, this paper has certain limitations. The scope of the analysis was restricted to English and Korean documents, and the dataset size was relatively limited. Additionally, while SIFT and the GLCM were used as the primary detection methods, further research should compare these findings with results from deep learning-based forgery detection techniques. Future research should focus on developing large-scale Korean-language document forgery datasets, exploring transformer-based deep learning models, and assessing the impact of multilingual training approaches to improve generalization. In conclusion, this paper serves as a foundational step toward enhancing the applicability of digital document forgery detection models in multilingual contexts. By developing more sophisticated detection models and constructing diverse language-based datasets, future research can contribute to the advancement of reliable and globally applicable forgery detection technology. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Type of Data**} & \multicolumn{3}{c}{**Training**} & \multicolumn{2}{c}{**Test**} \\ \cline{3-6}  & & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\  & & **(5-Fold CV, 95\% CI)** & **(5-Fold CV, 95\% CI)** & **(95\% CI)** & **(95\% CI)** \\ \hline \multirow{10}{*}{[22]} & Training—English & 0.63 & 0.61 & 0.67 & 0.65 \\  & Test—English & (0.60–0.66) & (0.58–0.64) & (0.63–0.70) & (0.61–0.68) \\  & Training—Korean & 0.64 & 0.64 & 0.68 & 0.67 \\  & Test—Korean & (0.61–0.66) & (0.61–0.66) & (0.64–0.71) & (0.63–0.70) \\  & Training—English & 0.63 & 0.61 & 0.58 & 0.58 \\  & Test—Korean & (0.60–0.66) & (0.58–0.64) & (0.54–0.61) & (0.54–0.61) \\  & Training—Korean & 0.64 & 0.64 & 0.39 & 0.34 \\  & Test—English & (0.61–0.66) & (0.61–0.66) & (0.35–0.42) & (0.30–0.36) \\ \hline \multirow{10}{*}{[7]} & Training—English & 0.91 & 0.91 & 0.82 & 0.82 \\  & Test—English & (0.89–0.93) & (0.89–0.93) & (0.78–0.86) & (0.78–0.86) \\  & Training—Korean & 0.86 & 0.86 & 0.66 & 0.68 \\  & Test—Korean & (0.83–0.89) & (0.83–0.89) & (0.63–0.69) & (0.65–0.71) \\  & Training—English & 0.91 & 0.91 & 0.54 & 0.53 \\  & Test—Korean & (0.89–0.93) & (0.89–0.93) & (0.50–0.58) & (0.49–0.57) \\  & Training—Korean & 0.86 & 0.86 & 0.52 & 0.56 \\  & Test—English & (0.83–0.89) & (0.83–0.89) & (0.48–0.56) & (0.52–0.60) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
**Author Contributions:** Conceptualization, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing [PERSON] and [PERSON].