Image spam hunter

Yan Gao*, Ming Yang, Xiaonan Zhao, Bryan A Pardo, Ying Wu, Thrasyvoulos N Pappas, Alok Nidhi Choudhary

*Corresponding author for this work

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

41 Scopus citations

Abstract

Spammers are constantly creating sophisticated new weapons in their arms race with anti-spam technology, the latest of which is image-based spam. The newest image-based spam uses simple image processing technologies to vary the content of individual messages, e.g. by changing foreground colors, backgrounds, font types, or even rotating and adding artifacts to the images. Thus, they pose great challenges to conventional spam filters. In this paper, we propose a system using a probabilistic boosting tree to determine whether an incoming image is a spam or not based on global image features, i.e. color and gradient orientation histograms. The system identifies spam without the need for OCR and is robust in the face of the kinds of variation found in current spam images. Evaluation results show the system correctly classifies 90% of spam images while mislabeling only 0.86% of non-spam images as spam.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1765-1768
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

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

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Image spam
  • Probabilistic boosting tree

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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