# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 2 Related Work  
Ref. [1] conducted an extensive survey on machine learning techniques for cybersecurity, covering a decade of research. The authors systematically reviewed various ML applications in intrusion detection, spam filtering, and malware detection across computer and mobile networks. Their comprehensive study outlined the effectiveness of different ML methods, including deep learning and traditional classifiers, and highlighted critical evaluation metrics and common cybersecurity datasets. However, the study focused mainly on text-based threats and did not explore the complexities of image-based spam detection, which remains a growing challenge in modern digital communications. As machine learning (ML) models can learn from complex datasets, they have been used more frequently to detect picture spam to address this issue. To detect patterns and identify spam text in images, they can be trained on large datasets. In the research on image spam identification, multiple machine learning models, including Support Vector Machines (SVMs), Naive Bayes, Logistic Regression, and XGBoost, have shown great potential in spam detection [2]. However, most existing studies lack a unified preprocessing pipeline or comparative analysis, making it difficult to assess the relative performance of these models under consistent conditions. Ref. [3] has shown that machine learning techniques can improve image spam detection by analyzing low-level image texture features. This research evaluated various ML classifiers, including decision trees, Support Vector Machines, Bayesian Networks, and random forests, on publicly available datasets. The authors observed that the random forest model outperformed other models and achieved 98.6% precision. Despite strong results, the study's reliance on basic texture features could limit its robustness. The research carried out in [4] proposed an image-based spam detection using a deep learning model to increase performance. They added four sub-models to an already existing spam detection model. The research conveyed that added submodels increased the capability to detect spam images but remained computationally heavy and hard to deploy in real time. The authors in [5] evaluate the effectiveness of ML methods for email spam detection. They discuss how models such as Logistic Regression and Naive Bayes can achieve up to 99% accuracy. The findings highlight the importance of ML in classifying spam versus non-spam messages. However, email-based spam detection differs significantly from image spam detection in the handling features. In [6], the authors were focused on SMS spam detection, proposing a hybrid deep learning model combining convolutional neural networks (CNNs) and gated recurrent units (GRUs). They achieved an accuracy of 99.07% and highlighted the effectiveness of hybrid architectures in handling textual data, emphasizing the importance of optimal hyperparameter tuning for improved spam detection performance. In a detailed study on the weaknesses and capabilities of several models, the authors in [7] used the spam email dataset along with numerous approaches and identified challenges and limitations when using ensemble frameworks, such as the need for hyperparameter tuning for good accuracy and performance. Similarly, the research conducted in [8] suggested combining random forest and decision tree to obtain better accuracy for spam classification. These studies mostly address spam in structured text forms and do not explore spam that blends images and text. In [9], the authors proposed an approach that combines optical character recognition, natural language processing, and a machine learning algorithm to detect image spam more effectively. This combination improves the models' performance and also provides a more robust method for analyzing image-based spam content. The research does not address the performance implications of utilizing images that contain styled or formatted text.