Network Security White Papers
Using Visual Features for Anti-Spam Filtering
Overview Unsolicited Commercial Email (UCE), also known as spam, has been a major problem on the Internet. In the past, researchers have addressed this problem as a text classification or categorization problem. However, as spammers' techniques continue to evolve and the genre of email content becomes more and more diverse, text-based anti-spam approaches alone are no longer sufficient. This paper proposes a novel anti-spam system which utilizes visual clues, in addition to text information in the email body, to determine whether a message is spam. They analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. They then propose using one-class Support Vector Machines (SVM) as the underlying base classifier for anti-spam filtering.
| Publisher | University of California | File Format | |
|---|---|---|---|
| Date Published | January 2009 | ||
| Format | White Papers | ||
| Topics | |||
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