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Fusion of Text and Image Features: A New Approach to Image Spam Filtering

  • Congfu Xu
  • Kevin Chiew
  • Yafang Chen
  • Juxin Liu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 124)

Abstract

While enjoying the convenience of email communications, many users have also experienced annoying email spam. Even if the current spam detecting approaches have gained a competitive edge against text-based email spam, they still face the challenge arising from image-based spam (image spam in short). Image spam normally includes embedded images that contain the spam messages in binary format rather than text format and cost more storage and bandwidth resources. In this paper, we propose a hybrid image spam filtering framework to detect spam images based on both extracted text and image features. Our experimental results show that our approach achieves significant improvement in detection accuracy as compared with other methods that simply use text or image features, and works robustly in an environment with either complex background or compression artifact.

Keywords

Support Vector Machine Text Region Color Saturation Spam Detection Email Spam 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Congfu Xu
    • 1
  • Kevin Chiew
    • 2
  • Yafang Chen
    • 1
  • Juxin Liu
    • 1
  1. 1.Institute of Artificial IntelligenceZhejiang UniversityHangzhouChina
  2. 2.School of EngineeringTan Tao UniversityLong AnVietnam

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