OcrV1, PascalFrancis, Curation, bibRecord, 000646

A survey and experimental evaluation of image spam filtering techniques

Identifieur interne : 000646 ( PascalFrancis/Curation ); précédent : 000645; suivant : 000647

A 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.

RBID : Pascal:11-0294382

Descripteurs français

Pascal (Inist)
- Traitement image, Filtrage, Reconnaissance caractère, Reconnaissance optique caractère, Analyse contenu, Communication multimédia, Catégorisation, Vision ordinateur, Reconnaissance forme.

English descriptors

KwdEn :
- Categorization, Character recognition, Computer vision, Content analysis, Filtering, Image processing, Multimedia communication, Optical character recognition, Pattern recognition.

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.

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C01	`01`		`ENG`	@0 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.
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C03	`05`	`X`	`SPA`	`@0 Análisis contenido @5 05`
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C03	`09`	`X`	`ENG`	`@0 Pattern recognition @5 09`
C03	`09`	`X`	`SPA`	`@0 Reconocimiento patrón @5 09`
N21				`@1 199`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

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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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A survey and experimental evaluation of image spam filtering techniques

A survey and experimental evaluation of image spam filtering techniques

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Abstract

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