Publication Date

Spring 2017

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Mark Stamp

Second Advisor

Thomas Austin

Third Advisor

Aikaterini Potika


image spam, machine learning


Email is one of the most common forms of digital communication. Spam can be de ned as unsolicited bulk email, while image spam includes spam text embedded inside images. Image spam is used by spammers so as to evade text-based spam lters and hence it poses a threat to email based communication. In this research, we analyze image spam detection methods based on various combinations of image processing and machine learning techniques.