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Eighth International Conference on Document Analysis and Recognition (ICDAR'05)
Image Analysis for Efficient Categorization of Image-based Spam E-mail
Seoul, Korea
August 31-September 01
ISBN: 0-7695-2420-6
Hrishikesh B. Aradhye, SRI International, Menlo Park, CA, USA
Gregory K. Myers, SRI International, Menlo Park, CA, USA
James A. Herson, SRI International, Menlo Park, CA, USA
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.
Citation:
Hrishikesh B. Aradhye, Gregory K. Myers, James A. Herson, "Image Analysis for Efficient Categorization of Image-based Spam E-mail," icdar, pp.914-918, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005
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