# Mmp1
## 2 Related Works
### Image-Based Feature Extraction Techniques  
Image spam was introduced by spammers in 2006 to evade text-based spam filters. It generally consists of embedding texts within images. Thus, various computer vision and pattern recognition techniques have been recently suggested to detect image spam.  
Text area (TA), low-level (LL), image similarity (IS), image regions similarity (RS), image metadata (M), and text obfuscation (T) are the most discriminative features extracted from an image. These features are used by many studies to train different machine learning models for classifying image-based e-mail as either ham or spam. The authors of [20] used the TA and LL as features to train the one-class SVM classifier, whereas [PERSON]. [21; 22] adopted SVM for the same features. In addition, [PERSON]. [23; 24; 25] suggested the use of LL and IS features to train different classifiers including one-class SVM, SVM, and probabilistic boosting tree. Furthermore [PERSON]. [26] used the LL and M features to train three models: max entropy, naive Bayes, and decision tree.  
The authors of [27] used two approaches to classify an image as either spam or ham: principal component analysis (PCA) and support vector machines (SVM). The first approach was used to construct the eigenspace from a training set of spam images. In the testing phase, a new image was projected onto this constructed eigenspace, and then this image was classified as spam if its score, which was the Euclidean distance between the scoring matrix of the training set and the weight vector of the test image, was below a certain threshold. In the second approach, they extracted 21 different features from images used as input to a linear SVM classifier. They obtained good results using PCA and SVM on the Image Spam Hunter (ISH) dataset, but these techniques did not detect spam images in their new improved dataset, referred to as Challenge Dataset 1. Moreover, [PERSON]. [28] used a linear SVM to classify the image as ham or spam using 38 features including metadata, color, texture, shape, and noise. They achieved good performance on the ISH and Dredze datasets, but they failed to detect spam images on their improved dataset, referred to as Challenge Dataset 2. Despite the good results, [PERSON]. [29] stated that the features used by these previous works were computationally extensive to extract. The authors of [29] proposed the use of the Canny edge detector combined with the raw image as features of images, which were then fed to a CNN model. Their experiment showed good results on the ISH dataset compared to improved Challenge Dataset 1 and 2. In addition, [PERSON]. [30] suggested a new image-based spam filtering architecture based on the convolutional neural network (CNN). The image features were generated from the last layer of CNN and fed to the support vector machine (SVM) classifier.  
In addition, [PERSON]. [31] proposed the DeepCapture architecture for image spam detection, which was based on the convolutional neural network (CNN) model and the XGBoost classifier. This method achieved the highest _F1-score_ compared to existing spam detection models. Furthermore, [PERSON]. [32] used the DCNN (deep convolutional neural network) and pretrained CNN for spam image detection. Three different datasets were used to prove the effectiveness of their proposed models.  
Despite the improvements achieved in both image-based and text-based feature extraction techniques for distinguishing spam from ham, there is an important lack of multimodal systems which combine the two feature extraction techniques to deal with hybrid spam e-mails. Concatenating both image and text features into one vector, before the classification step, provides a robust representation for each e-mail.