Work

Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis

Year: 2025

Type: article

Abstract: Image-based spam poses a significant challenge for traditional text-based filters, as malicious content is often embedded within images to bypass keyword detection techniques. This study investigates and compares the performance of six machine learning models—ResNet50, XGBoost, Logistic Regression, LightGBM, Support Vector Machine (SVM), and VGG16—using a curated dataset containing 678 legitimate (ham) and 520 spam images. The novelty of this research lies in its comprehensive side-by-side evaluation of diverse models on the same dataset, using standardized dataset preprocessing, balanced data splits, and validation techniques. Model performance was assessed using evaluation metrics such as accuracy, receiver operating characteristic (ROC) curve, precision, recall, and area under the curve (AUC). The results indicate that ResNet50 achieved the highest classification performance, followed closely by XGBoost and Logistic Regression. This work provides practical insights into the strengths and limitations of traditional, ensemble-based, and deep learning models for image-based spam detection. The findings can support the development of more effective and generalizable spam filtering solutions in multimedia-rich communication platforms.

Source: Applied Sciences

DOI: 10.3390/app15116158

Authors: Mahnoor Jamil, Hristina Mihajloska Trpcheska, Aleksandra Popovska-Mitrovikj, Vesna Dimitrova, Reiner Creutzburg

Institutions: Kadir Has University, Ss. Cyril and Methodius University in Skopje, SRH University of Applied Sciences Heidelberg, Brandenburg University of Applied Sciences

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: European Commission