The use of Optical Character Recognition (OCR) software in spam filtering

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The use of Optical Character Recognition (OCR)
software in spam filtering

• By Scott Conrad

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Spam is changing from text only to multimedia
enhanced

• legitimate message-senders have added multimedia
  content, particularly images, to text-based
  emails
• source Using Visual Features for Anti-Spam
  Filtering, 2005

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Instances of spam/phishing
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Instances of spam/phishing
source Spam Filtering Based On The Analysis Of
Text Information Embedded Into Images, 2006
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Optical Character Recognition (OCR)

• Pattern recognition to interpret pictures as text

source Using Visual Features for Anti-Spam
Filtering, 2005
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OCR papers

• Using Visual Features for Anti-Spam Filtering
• Ching-Tung Wu, Kwang-Ting Cheng, Qiang Zhu, and
  Yi-Leh Wu
• Spam Filtering Based On The Analysis Of Text
  Information Embedded Into Images
• by Giorgio Fumera, Ignazio Pillai, and Fabio
  Roli
• Learning Fast Classifiers for Image Spam
• by Mark Dredze, Reuven Gevaryahu, and Ari
  Elias-Bachrach
• Image Analysis for Efficient Categorization of
  Image-based Spam E-mail
• by Hrishikesh B. Aradhye, Gregory K. Myers, and
  James A. Herson

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General Methodology

• Using Visual Features for Anti-Spam Filtering
• Created a Bayesian spam filter for Thunderbird
• Ran this filter against a spam archive
• Added in OCR capabilities
• Ran the filter against the spam archive again
• The detection rate rose from 47.7 to 84.6

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Counter measures to OCR

• Image Spam Filtering by Content Obscuring
  Detection
• Battista Biggio, Giorgio Fumera, Ignazio Pillai,
  and Fabio Roli
• Filtering Image Spam with Near-Duplicate
  Detection
• Zhe Wang, William Josephson, Qin Lv, Moses
  Charikar, and Kai Li

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Images from paper 2
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Project Goals

• Research different multimedia-based spam filters
  and any counter measures that spammers have
  created to use against these filters
• Attempt to recreate one of the spam filters to
  verify the results