# A Spam Filtering Method Based on Multi-Modal Fusion
## 1 Introduction  
Spam can be defined as an email which contains unsolicited mail [1]. With the rapid development of the Internet, Internet users are increasingly using emails to communicate. At the same time, the issue of spam is getting worse, in which the purpose of most spam is to solicit the recipients for money. In order to achieve this, the products they provide claim to miraculously cure health problems such as diabetes, obesity and hair loss. They may be of any nature, whether it is an advertisement, a text email, an image email or a email that contains text and image data. According to the spam analysis report of Kaspersky Lab, a well-known organization in the security field, the average proportion of global spam in total emails were as high as 56.63% or more in 2017 [2]. This phenomenon indicates that spam is flooding the entire network, which brings inconvenience to cyber citizens. For text spam or image spam, the single-modal spam filtering systems have a high detection rate, while, in order to escape detection, spammers may insert junk information into the multi-modal part of an email, which we call it hybrid spam, to reduce the detection rate of the single-modal spam filtering systems, ultimately achieving the purpose of evading detection. For hybrid spam, it is more harmful than traditional spam because it contains more information than traditional spam, and it requires more network bandwidth and storage space for forwarding and delivery of the mailbox servers. Moreover, viruses or unsolicited information carried by hybrid spam are more difficult to detect, which brings tremendous information security risks to people's communication. Therefore, it is extremely important to learn how to effectively identify hybrid spam. In machine learning and cybersecurity communities, anti-spam methods have been studied for many years [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. These methods roughly are classified into three categories: (1) text-based spam detection; (2) image-based spam detection; and (3) multi-modal spam detection. The first and second categories primarily use the textual content or image content of an email to filter spam, respectively. However, the last category processes both the textual and image content of an email to filter spam. For text-based spam detection, the specific research contents are as follows: [PERSON] [3] uses the decision tree method to filter spam, which uses the RLM (R. LOPEZ DE MANTARAS) distance method instead of the information gain method to select text features. The experimental results show that the accuracy and recall rate of this method are above 88%. [PERSON] [4] employs multiple weak classifiers to get multiple classification probability values of an email as spam, thereinto every weak classifier uses a logarithmic regression method to obtain the classification probability value. Eventually, using a boosting method to combine the multiple classification probability values to get a real value of the email as spam, which is compared to a threshold to determine whether the email is spam or not. The remaining methods use other methods to filter spam, such as Bayesian algorithm [5; 6; 7; 8], k-Nearest Neighbor (k-NN) algorithm [9] and deep learning algorithm [10]. These methods have good performance for text spam detection. However, if most of the junk information is included in the image section, and the text section does not contain the junk information or only contains a small amount of it, will cause the spam filtering systems to treat it as a normal email. For image-based spam detection, some methods have also been proposed and demonstrated good performance. [PERSON] [12] proposed a method to use the convolutional neural network to automatically extract image features and detect spam. [PERSON] [13] proposed a method that uses a multi-model combination method to identify image spam. [PERSON] [14] proposed an supported vector machine (SVM) method based on Gaussian kernel to filter spam.