Image Analysis for Efficient Categorization of Image-based Spam E-mail

  • Authors:
  • Hrishikesh B. Aradhye;Gregory K. Myers;James A. Herson

  • Affiliations:
  • SRI International, Menlo Park, CA, USA;SRI International, Menlo Park, CA, USA;SRI International, Menlo Park, CA, USA

  • Venue:
  • ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
  • Year:
  • 2005

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Abstract

To circumvent prevalent text-based anti-spam filters, spammers have begun embedding the advertisement text in images. Analogously, proprietary information (such as source code) may be communicated as screenshots to defeat text-based monitoring of outbound e-mail. The proposed method separates spam images from other common categories of e-mail images based on extracted overlay text and color features. No expensive OCR processing is necessary. Our method works robustly in spite of complex backgrounds, compression artifacts, and a wide variety of formats and fonts of overlaid spam text. It is also demonstrated successfully to detect screenshots in outbound e-mail.