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

The use of Optical Character Recognition (OCR) software in spam filtering. By: Scott Conrad. Spam is changing from text only to multimedia enhanced. legitimate message-senders have added multimedia content, particularly images, to text-based emails

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

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  1. The use of Optical Character Recognition (OCR) software in spam filtering By: Scott Conrad

  2. 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

  3. Instances of spam/phishing

  4. Instances of spam/phishing source: “Spam Filtering Based On The Analysis Of Text Information Embedded Into Images”, 2006

  5. Optical Character Recognition (OCR) • Pattern recognition to interpret pictures as text source: “Using Visual Features for Anti-Spam Filtering”, 2005

  6. 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

  7. 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%

  8. 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

  9. Images from paper 2

  10. 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

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