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Using visual features for anti-spam filtering

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4 Author(s)
Ching-Tung Wu ; California Univ., Santa Barbara, CA, USA ; Kwang-Ting Cheng ; Qiang Zhu ; Yi-Leh Wu

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. In this paper, we propose 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. We analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. We then propose using one-class support vector machines (SVM) as the underlying base classifier for anti-spam filtering. The experimental results demonstrate that the proposed system can add significant filtering power to the existing text-based anti-spam filters.

Published in:

Image Processing, 2005. ICIP 2005. IEEE International Conference on  (Volume:3 )

Date of Conference:

11-14 Sept. 2005