# Mmp1
## 1 Introduction  
Spamming, which is defined as the behavior of sending unsolicited messages to a large number of people, is currently spreading rapidly. The spam overflow has led academic researchers to consider this phenomenon as an interesting research area because reputational damage and financial disruption are among the most important activities of spammers [1]. Accordingly, efficient and reliable spam e-mail filtering systems are required for businesses, as well as individuals. These systems should necessarily employ powerful techniques to deal with the increasing number of undesirable e-mails. Spam e-mail detection has been extensively studied for a long time. The existing spam e-mail filtering systems are divided into two main categories: unimodal and multimodal. The first category comprises two types: text-based [2] and image-based [3]. The former is developed to detect spam e-mails containing only text, while the latter is built to deal with embedded text within images, knowing that it was created by spammers to evade text-based systems. The second category, multimodal systems, is designed for detecting spam e-mails containing both image and text, known as hybrid spam e-mails [4]. The hybrid spam e-mail is more harmful and complex as compared to image-based and text-based spam e-mail due to the amount of information within. This makes it a challenging task. Consequently, the hybrid spam e-mail demands efficient algorithms for treating both textand image content. The multimodal system is aimed at generating feature vectors from both text and image modalities, before combining them at the feature level or decision level. To the best of our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. The authors of [5] used the term frequency inverse document frequency (TF-IDF) to extract text features, while image features represented the color, image, and file attributes. First, the P-SVM (support vector machine with a confidence P) was adopted to separately learn text and image modalities. Then, the output of the multi-classifier was fed to the SVM (support vector machine) model to classify an e-mail as either spam or ham. Additionally, Ref. [4] fused two classification probability scores. These values were generated from image and text parts by consecutively using a convolutional neural network (CNN) and a long short-term memory (LSTM) model. The authors of [6] used the CNN model to construct two different multimodal architectures to identify an e-mail as spam or ham. The first concatenated the last layer of CNN of both image and text features to feed them to a fully connected layer using the softmax function. Using the same features, the second architecture applied decision-level fusion to classify an e-mail as either spam or ham. However, despite the significant performance of the previous models, they still suffer from many drawbacks that are not yet solved. Firstly, the decision-level fusion not only demands many learning steps but also loses correlation in mixed feature space [7]. Indeed, each modality is separately trained using different classification models before the fusion step. In addition, the output classification scores are fused to make a final decision. Secondly, the TF-IDF feature extraction technique has very limited applicability. This method is only based on the frequency of a word in a document. However, it does not take the word order into account, which leads to misclassification. Feature extraction is a crucial step in spam detection. Indeed, it has a negative impact on the performance of the model. Recently, many improvements of great importance have been accomplished while using machine learning (ML) and deep learning (DL) techniques. The main contribution of this paper is the proposition of a new multimodal architecture based on PV-DBOW, CNN, and RF. It consists of generating feature vectors from both text and image of the same e-mail by consecutively using the PV-DBOW and CNN models.