# MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images
## 4 Email Document Picture (EDP) Dataset  
The Email Document Picture (EDP) dataset has been specifically designed for the evaluation of spam detection models. While other existing email datasets, such as the Spam Assassin dataset ([[https://spamassassin.apache.org/old/publiccorpus/](https://spamassassin.apache.org/old/publiccorpus/)]([https://spamassassin.apache.org/old/publiccorpus/](https://spamassassin.apache.org/old/publiccorpus/)), accessed on 1 June 2022) and the Enron Email dataset ([[https://www.kaggle.com/datasets/wcukierski/enron-email-dataset](https://www.kaggle.com/datasets/wcukierski/enron-email-dataset)]([https://www.kaggle.com/datasets/wcukierski/enron-email-dataset](https://www.kaggle.com/datasets/wcukierski/enron-email-dataset)), accessed on 1 June 2022), have been used in the past, they often lack image components and have limited multimodal data. To overcome these limitations, we introduced the EDP dataset, which includes both textual and visual elements within email samples. The EDP dataset stands out for several reasons, distinguishing it from prior multimodal spam datasets:  
1. The EDP dataset is currently the largest multilingual multimodal spam detection dataset available. While [PERSON] et al. [23] introduced the Heterogeneous dataset with a larger number of samples, it does not include multilingual text. 2. Images in the EDP dataset are unique, as they are document images converted from emails. This differs from other multimodal spam datasets where images are extracted from emails. Figure 6 illustrates the distinction between these two dataset types. 3. The EDP dataset features varying numbers of images within each data sample. Figure 7 provides sample illustrations from the EDP dataset, showcasing examples of both spam and non-spam (ham) emails. The EDP dataset is a synthetic compilation created by combining data from various sources. It includes a portion from the Spam Assassin dataset, another portion collected by our team, and the remaining data are synthesized. The synthesis process involves the random selection of text and image materials to construct the email samples. Text materials are sourced from repositories such as the Enron email dataset, the Spam Classification Dataset CSV ([[https://www.kaggle.com/datasets/balakal8/email-spam-classification-dataset-csv](https://www.kaggle.com/datasets/balakal8/email-spam-classification-dataset-csv)]([https://www.kaggle.com/datasets/balakal8/email-spam-classification-dataset-csv](https://www.kaggle.com/datasets/balakal8/email-spam-classification-dataset-csv)), accessed on 1 June 2022), the Multilingual Spam dataset ([[https://www.kaggle.com/datasets/rajanthpatel/multilingual-spam-data](https://www.kaggle.com/datasets/rajanthpatel/multilingual-spam-data)]([https://www.kaggle.com/datasets/rajanthpatel/multilingual-spam-data](https://www.kaggle.com/datasets/rajanthpatel/multilingual-spam-data)), accessed on 1 June 2022) and the Ling-Spam dataset ([[https://www.kaggle.com/datasets/mandygu/lingspam-dataset](https://www.kaggle.com/datasets/mandygu/lingspam-dataset)]([https://www.kaggle.com/datasets/mandygu/lingspam-dataset](https://www.kaggle.com/datasets/mandygu/lingspam-dataset)), accessed on 1 June 2022). On the other hand, image materials are randomly chosen from the COCO Caption dataset ([[https://cocodataset.org/#download](https://cocodataset.org/#download)]([https://cocodataset.org/#download](https://cocodataset.org/#download)), accessed on 1 June 2022) the Image Spam Hunter dataset [4], and the Dredze dataset ([[https://www.cs.jhu.edu/](https://www.cs.jhu.edu/)]([https://www.cs.jhu.edu/](https://www.cs.jhu.edu/))\(\sim\)dredze/datasets/image_spam/ accessed on 1 June 2022). Figure 6: (a) Displays an email document from the EDP dataset, containing both image and text components combined into a single email sample; (b) is a sample from the heterogeneous dataset, where the image and text components of an email are stored separately.