# MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images
## 6 Discussion  
### Theoretical and Practical Implications  
Our research contributes to the field of multimodal spam detection by addressing several key challenges. Firstly, our proposed MMTD model is capable of handling spam in multiple languages, which is a significant advancement compared to existing methods that primarily focus on English. This enables our model to effectively detect spam in diverse linguistic contexts and enhances its applicability in global email systems.  
Secondly, our approach incorporates document image encoding as a novel modality in spam detection. By converting the entire email into a single document image, our model gains the ability to capture global information and contextual cues present in emails. This approach overcomes the limitations of existing methods that only process a single image or solely focus on textual content. The inclusion of document image encoding improves the robustness and accuracy of spam detection, especially when dealing with image-based spam or plain text emails.  
Thirdly, our multimodal fusion module effectively combines the features extracted from both text and image modalities, resulting in improved spam detection performance. The fusion of multimodal features allows for a comprehensive analysis of emails, leveraging the complementary information provided by text and images. This fusion-based approach  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Fold** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{CLIP} & 1 & 0.865 & 0.870 & 0.870 & 0.870 \\  & 2 & 0.883 & 0.880 & 0.880 & 0.880 \\  & 3 & 0.843 & 0.870 & 0.840 & 0.840 \\  & 4 & 0.875 & 0.880 & 0.870 & 0.870 \\  & 5 & 0.750 & 0.820 & 0.750 & 0.740 \\  & **mean** & **0.843** & **0.864** & **0.842** & **0.840** \\ \hline \multirow{8}{*}{Vilt} & 1 & 0.784 & 0.810 & 0.790 & 0.780 \\  & 2 & 0.770 & 0.810 & 0.780 & 0.770 \\  & 3 & 0.845 & 0.850 & 0.850 & 0.840 \\  & 4 & 0.785 & 0.810 & 0.790 & 0.780 \\  & 5 & 0.789 & 0.810 & 0.790 & 0.790 \\  & **mean** & **0.795** & **0.818** & **0.800** & **0.792** \\ \hline \multirow{8}{*}{MMA-MF} & 1 & 0.680 & 0.700 & 0.680 & 0.670 \\  & 2 & 0.556 & 0.610 & 0.570 & 0.520 \\  & 3 & 0.572 & 0.590 & 0.570 & 0.540 \\  & 4 & 0.715 & 0.730 & 0.720 & 0.710 \\  & 5 & 0.681 & 0.700 & 0.680 & 0.670 \\  & **mean** & **0.641** & **0.666** & **0.644** & **0.622** \\ \hline \multirow{8}{*}{MMTD} & 1 & 0.907 & 0.910 & 0.910 & 0.910 \\  & 2 & 0.902 & 0.910 & 0.900 & 0.900 \\ \cline{1-1}  & 3 & 0.906 & 0.910 & 0.910 & 0.910 \\ \cline{1-1}  & 4 & 0.913 & 0.920 & 0.910 & 0.910 \\ \cline{1-1}  & 5 & 0.905 & 0.910 & 0.900 & 0.900 \\ \cline{1-1}  & **mean** & **0.906** & **0.912** & **0.906** & **0.906** \\ \hline \hline \end{tabular}
\end{table}
Table 12: Experimental results on the noisy test dataset.  
outperforms single-modal methods and demonstrates the benefits of integrating multiple modalities for more accurate spam detection.  
From a practical perspective, our research provides a valuable tool for enhancing email security. By accurately detecting spam across different languages and modalities, our model enables email providers and users to effectively filter out unwanted and potentially harmful messages. This can significantly reduce the impact of spam on productivity, privacy, and online security.