Improved spam filtering by extraction of information from text embedded image e-mail
Seongwook Youn, Dennis J. McLeod · 2009
The increase of image spam, a kind of spam in which the text message is embedded into an attached image to defeat spam filtering techniques, is becoming an increasingly major problem. For nearly a decade, content based filtering using text classification or machine learning has been a major trend of antispam filtering systems. A Key technique being used by spammers is to embed text into image(s) in spam email. In [4], we proposed two levels of ontology spam filters: a first level global ontology filter and a second level user-customized ontology filter. However, that previous system handles only text e-mail and the percentage of attached images is increasing sharply. The contribution of the paper is that we add an image e-mail handling capability to the previous anti-spam filtering system, enhancing the effectiveness of spam filtering.