A survey and experimental evaluation of image spam filtering techniques
Identifieur interne : 000577 ( Main/Exploration ); précédent : 000576; suivant : 000578A survey and experimental evaluation of image spam filtering techniques
Auteurs : Battista Biggio [Italie] ; Giorgio Fumera [Italie] ; Ignazio Pillai [Italie] ; Fabio Roli [Italie]Source :
- Pattern recognition letters [ 0167-8655 ] ; 2011.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
In their arms race against developers of spam filters, spammers have recently introduced the image spam trick to make the analysis of emails' body text ineffective. It consists in embedding the spam message into an attached image, which is often randomly modified to evade signature-based detection, and obfuscated to prevent text recognition by OCR tools. Detecting image spam turns out to be an interesting instance of the problem of content-based filtering of multimedia data in adversarial environments, which is gaining increasing relevance in several applications and media. In this paper we give a comprehensive survey and categorisation of computer vision and pattern recognition techniques proposed so far against image spam, and make an experimental analysis and comparison of some of them on real, publicly available data sets.
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">In their arms race against developers of spam filters, spammers have recently introduced the image spam trick to make the analysis of emails' body text ineffective. It consists in embedding the spam message into an attached image, which is often randomly modified to evade signature-based detection, and obfuscated to prevent text recognition by OCR tools. Detecting image spam turns out to be an interesting instance of the problem of content-based filtering of multimedia data in adversarial environments, which is gaining increasing relevance in several applications and media. In this paper we give a comprehensive survey and categorisation of computer vision and pattern recognition techniques proposed so far against image spam, and make an experimental analysis and comparison of some of them on real, publicly available data sets.</div>
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