A comprehensive approach to image spam detection: From server to client solution

Yan Gao*, Alok Choudhary, Gang Hua

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Image spam is a type of e-mail spam that embeds spam text content into graphical images to bypass traditional text-based e-mail spam filters. To effectively detect image spam, it is desirable to leverage image content analysis technologies. However, most previous works of image spam detection focus on filtering the image spam on the client side. We propose a more desirable comprehensive solution which embraces both server-side filtering and client-side detection to effectively mitigate image spam. On the server side, we present a nonnegative sparsity induced similarity measure for cluster analysis of spam images to filter the attack activities of spammers and fast trace back the spam sources. On the client side, we employ the principle of active learning where the learner guides the users to label as few images as possible while maximizing the classification accuracy. The server-side filtering identifies large image clusters as suspicious spam sources and further analysis can be performed to identify the real sources and block them from the beginning. For those spam images which survived the server-side filter, our active learner on the client side will further guide the users to interactively and efficiently filter them out. Our experiments on an image spam data-set collected from the e-mail server of our department demonstrate the efficacy of the proposed comprehensive solution.

Original languageEnglish (US)
Article number5585752
Pages (from-to)826-836
Number of pages11
JournalIEEE Transactions on Information Forensics and Security
Volume5
Issue number4
DOIs
StatePublished - Dec 2010

Keywords

  • Active learning
  • clustering
  • image recognition
  • image spam
  • spam filtering
  • sparse representation

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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