# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 2 Related Work
### Comparative Analysis of Machine Learning Models  
Supervised learning models have to go through numerous research cycles to achieve the correct output. As the name suggests, the supervisor is the one who instructs the model on what the input is and what the corresponding output should be. The supervisor primarily trains the model with labels [18]. Then, models use these labels for classification purposes. The supervised learning models are trained on a dataset, which is always divided into a training and a test set. The training set is the data passed into the model for the training, and the test data are used to test the accuracy of the model and how well it can classify. There are many supervised learning models that can predict continuous target variables. Logistic Regression is used for binary classification tasks. Furthermore, decision trees are also used for both classification and regression. Moreover, Support Vector Machine focuses on classification tasks by finding the optimal hyperplane to separate different classes. Furthermore, ensemble learning includes merging multiple models to create a stronger model with better accuracy. Popular ensemble methods include random forests, which are collections of decision trees used for classification and regression tasks; gradient boosting, which builds strong models by sequentially combining weaker ones for regression problems; and AdaBoost, an ensemble technique that improves weak classifiers for binary classification tasks. These ensemble methods have the strengths of multiple models to improve performance, making them powerful tools for complex tasks (Figure 1). For the comparative analysis given in this paper, we selected XGBoost, Support Vector Machine, ResNet50, Logistic Regression, LightGBM, and VGG16 to ensure a comprehensive  
Figure 1: Machine learning models [19]. evaluation of diverse learning paradigms, including classical machine learning, ensemble learning, and deep learning. Each model represents a unique approach to the classification of spam images. ResNet50 was selected for the comparative analysis due to its proven architectural strengths and robust performance in complex image classification tasks. Its ability to effectively manage structured data and deliver high accuracy with minimal false positives has been consistently demonstrated in prior research [13]. Additionally, ResNet50 integrates techniques such as gradient boosting and regularization, enhancing its predictive power while reducing the risk of overfitting. Support Vector Machines (SVMs) have also gained prominence as a reliable and widely adopted method for classification, particularly in image-based spam detection, due to their effectiveness in high-dimensional spaces and strong generalization capabilities. In [20], the authors explored the effectiveness of SVMs trained on a diverse set of image features to classify spam content. The research highlights the use of a linear SVM to analyze and quantify the relative importance of various visual features, demonstrating the model's ability to distinguish between spam and legitimate images with high precision. Drawing from these insights, we selected SVM for our comparative analysis due to its proven performance in image spam detection tasks. LightGBM is an ensemble model, developed by Microsoft, and is renowned for its speed and efficiency. It utilizes histogram-based learning and leaf-wise tree growth strategies to enable faster training and lower memory usage than traditional boosting methods. However, as highlighted in [21], the LightGBM model shows sensitivity to noisy data. LightGBM is also designed to use less memory by training faster, supporting distributed and parallel computing, and handling large data [22]. These characteristics make LightGBM excellent for image spam detection, where rapid processing of images is required. Furthermore, XGBoost was created by [PERSON] and [PERSON] [23], who drew up a robust methodology for regression and classification.