[1]CHEN Jun-wei,ZHANG L i-chun,LU¨Yue.A spam image f ilter ing system based on user-spec if ied image content[J].CAAI Transactions on Intelligent Systems,2008,3(5):416-422.
Copy

A spam image f ilter ing system based on user-spec if ied image content

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 3 Number of periods: 2008 5 Page number: 416-422 Column: 学术论文—智能系统 Public date: 2008-10-25
Title:
A spam image f ilter ing system based on user-spec if ied image content
Author(s):
CHEN Jun-wei;  ZHANG L i-chun;  LU¨Yue
Department of Computer Science and Technology, East China NormalUniversity, Shanghai 200241, China
Keywords:
user-specified image content;  image spam filtering;  image matching
CLC:
TP391. 4
DOI:
-
Abstract:
Spammers often embed text in images in innocuous seeming emails, resulting in large numbers of spam images which are difficult to filter out. To solve this p roblem, a spam image filtering system was developed that ap2 p lies user2specified identification of spam image content. First, users were asked to identify spam images from emails to generate a class of spam images. These imageswere then added to a blacklist. Next, images in incoming e2mailswere matched with images stored in the image blacklist. Finally, if a matching image was found, the e2mail was judged as spam because it contained images identified as spam by users. Testing with an e2mail server contai2 ning 3534 samp le images p roved the p roposed method effectively intercep ts spam e2mails
References:
[ 1 ] HEYMANN P, KOUTR IKA G, HECTOR G M. Fighting spam on socialweb sites: a survey of app roaches and future challenges [ J ]. IEEE Internet Computing, 2007, 11 ( 6) : 36245.
?[ 2 ] ARADHYE H B, MYERS G K, HERSON J A. Image a2 nalysis for efficient categorization of image2based spam e2 mail [ C ] / / Eighth International Conference on Document Analysis and Recognition ( ICDAR’05). Seoul, South Kore2 a, 2005: 9142918.
[ 3 ] KUMAGA IN, AR ITSUGIM. On app lying an image p ro2 cessing technique to detecting spam [ C ] / / Proceedings of the 21st International Conference on Data Engineering. To2 kyo, Japan, 2005: 1642168.
[ 4 ] B IGGIO B, FUMERA G, P ILLA I I, ROL I F. Image spam filtering using visual information [ C ] / / 14 th International Conference on Image Analysis and Processing ( ICIAP 2007). Modena, Italy, 2007: 1052110.
[ 5 ] HOLMES N. In defense of spam [ J ]. Computer, 2005, 38 (4) : 86288.
[ 6 ] LOWE D G. Object recognition from local scale2invariant features [ C ] / / International Conference on Computer Vi2 sion. Corfu, Greece, 1999: 115021157.
?[ 7 ] 游素亚,杨　静. 图像边缘检测技术的发展与现状[ J ]. 电子科技导报, 1995, (8) : 25228.
?YOU Suya, YANG J ing. Image edge detection and its de2 velopment [ J ]. Electronic Science & Technology Review, 1995 (8) : 25228.
?[ 8 ] V IVARELL I F, W ILL IAMS K I. Discovering hidden fea2 tureswith Gaussian p rocesses regression[C ] / / Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II. Cambridge, MA, USA: M IT Press, 1999: 6132619.
[ 9 ] BROWN M, LOWE D G. Invariant features from interest point group s [ C ] / / British Machine Vision Conference, BMVC 2002. Cardiff, Wales, 2002: 6562665.
?[ 10 ] LOWE D G. Distinctive image features from scale2invari2 ant keypoints [ J ]. International Journal of Computer Vi2 sion, 2004, 60 (11) : 912110.
[ 11 ] BEIS J , LOWE D G. Shape indexing using app roximate nearest2neighbour search in high dimensional spaces [C ] / / Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan, PR, USA, 1997: 100021006.
[ 12 ] DREDZE M, GEVARYAHU R, BACHRACH A E. Learning fast classifiers for image spam[C ] / / Proceedings of the Conference on E-mail and Anti2Spam ( CEAS). West Point, NY, US, 2007: 2552261.
Similar References:

Memo

-

Last Update: 2009-05-18

Copyright © CAAI Transactions on Intelligent Systems