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

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,...

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Bibliographic Details
Main Authors:	Mahnoor Jamil, Hristina Mihajloska Trpcheska, Aleksandra Popovska-Mitrovikj, Vesna Dimitrova, Reiner Creutzburg
Format:	Article
Language:	English
Published:	MDPI AG 2025-05-01
Series:	Applied Sciences
Subjects:	spam detection image spam machine learning Support Vector Machine XGBoost Logistic Regression
Online Access:	https://www.mdpi.com/2076-3417/15/11/6158
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Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis

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