A nonnegative sparsity induced similarity measure with application to cluster analysis of spam images

Yan Gao*, Alok Choudhary, Gang Hua

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Image spam is an email spam that embeds text content into graphical images to bypass traditional spam filters. The majority of previous approaches focus on filtering image spam from client side. To effectively detect the attack activities of the spammers and fast trace back the spam sources, it is also essential to employ cluster analysis to comprehensively filter the image emails on the server side. In this paper, we present a nonnegative sparsity induced similarity measure for cluster analysis of spam images. This similarity measure is based on an assumption that a spam image should be represented well by the nonnegative linear combination of a small number of spam images in the same cluster. It is due to the observation that spammers generate large number of varieties from a single image source with different image processing and manipulation techniques. Experiments on a spam image dataset collected from our department email server demonstrated the advantages of the proposed approach.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages5594-5597
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Cluster analysis
  • Image spam filtering
  • Nonnegative sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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