Revealing common sources of image spam by unsupervised clustering with visual features

Authors:
Chengcui Zhang;Wei-Bang Chen;Xin Chen;Gary Warner
Affiliations:
University of Alabama at Birmingham, Birmingham, AL;University of Alabama at Birmingham, Birmingham, AL;University of Alabama at Birmingham, Birmingham, AL;University of Alabama at Birmingham, Birmingham, AL
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

Citing 4
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Abstract

In this paper, we investigate image spam with data mining techniques in order to reveal the common sources of unsolicited emails. To identify the origins, a two-stage clustering method groups visually similar spam images by exploring their visual features, including color feature, layout feature, text layout, and background textures. We test the proposed approach under different settings and combinations of features and measure the performance with a modified F-measure.