Publication Date
Spring 2019
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Katerina Potika
Third Advisor
Fabio Di Troia
Keywords
image spam, deep learning
Abstract
Spam can be defined as unsolicited bulk email. In an effort to evade text-based spam filters, spammers can embed their spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply various machine learning and deep learning techniques to real-world image spam datasets, and to a challenge image spam-like dataset. We obtain results comparable to previous work for the real-world datasets, while our deep learning approach yields the best results to date for the challenge dataset.
Recommended Citation
Sharmin, Tazmina, "Deep Learning for Image Spam Detection" (2019). Master's Projects. 702.
DOI: https://doi.org/10.31979/etd.b8me-rqsv
https://scholarworks.sjsu.edu/etd_projects/702
