Detecting Image Spam Using Local Invariant Features and Pyramid Match Kernel

Presented at: 18th International World Wide Web Conference (WWW2009)

by Haiqiang Zuo, Weiming Hu, Ou Wu, Yunfei Chen, Guan Luo

Webpage: http://www2009.eprints.org/174/1/p1187.pdf

Image spam is a new obfuscating method which spammers invented to more effectively bypass conventional text based spam filters. In this paper, we extract local invariant features of images and run a one-class SVM classifier which uses the pyramid match kernel as the kernel function to detect image spam. Experimental results demonstrate that our algorithm is effective for fighting image spam.

Keywords: Poster Session


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/174


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