article

A survey of image spamming and filtering techniques

Authors:

Abdolrahman Attar,

Reza Moradi Rad,

Reza Ebrahimi AtaniAuthors Info & Claims

Artificial Intelligence Review, Volume 40, Issue 1

Pages 71 - 105

https://doi.org/10.1007/s10462-011-9280-4

Published: 01 June 2013 Publication History

Abstract

Many techniques have been proposed to combat the upsurge in image-based spam. All the proposed techniques have the same target, trying to avoid the image spam entering our inboxes. Image spammers avoid the filter by different tricks and each of them needs to be analyzed to determine what facility the filters need to have for overcoming the tricks and not allowing spammers to full our inbox. Different tricks give rise to different techniques. This work surveys image spam phenomena from all sides, containing definitions, image spam tricks, anti image spam techniques, data set, etc. We describe each image spamming trick separately, and by perusing the methods used by researchers to combat them, a classification is drawn in three groups: header-based, content-based, and text-based. Finally, we discus the data sets which researchers use in experimental evaluation of their articles to show the accuracy of their ideas.

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Cited By

Singh ASingh KChanu YThongam KSingh K(2022)An Improved Image Spam Classification Model Based on Deep Learning TechniquesSecurity and Communication Networks10.1155/2022/89054242022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8905424
Kim BAbuadbba SKim H(2020)DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationInformation Security and Privacy10.1007/978-3-030-55304-3_24(461-475)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-55304-3_24
Naiemi FGhods VKhalesi H(2019)An efficient character recognition method using enhanced HOG for spam image detectionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-03728-z23:22(11759-11774)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s00500-018-03728-z
Show More Cited By

A survey of image spamming and filtering techniques
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools
2. Social and professional topics
  1. Computing / technology policy

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Published In

cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 40, Issue 1

June 2013

104 pages

ISSN:0269-2821

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Copyright © Copyright © 2013 Springer Science+Business Media Dordrecht.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2013

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Cited By

Singh ASingh KChanu YThongam KSingh K(2022)An Improved Image Spam Classification Model Based on Deep Learning TechniquesSecurity and Communication Networks10.1155/2022/89054242022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8905424
Kim BAbuadbba SKim H(2020)DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationInformation Security and Privacy10.1007/978-3-030-55304-3_24(461-475)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-55304-3_24
Naiemi FGhods VKhalesi H(2019)An efficient character recognition method using enhanced HOG for spam image detectionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-03728-z23:22(11759-11774)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s00500-018-03728-z
Aiwan FZhaofeng Y(2018)Image spam filtering using convolutional neural networksPersonal and Ubiquitous Computing10.5555/3288897.328892722:5-6(1029-1037)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288897.3288927
Amir ASrinivasan BKhan A(2018)Distributed classification for image spam detectionMultimedia Tools and Applications10.1007/s11042-017-4944-y77:11(13249-13278)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11042-017-4944-y
Chen JZhao HYang JZhang JLi TWang K(2017)An intelligent character recognition method to filter spam images on cloudSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1811-521:3(753-763)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00500-015-1811-5
Shen JDeng RCheng ZNie LYan S(2015)On robust image spam filtering via comprehensive visual modelingPattern Recognition10.1016/j.patcog.2015.02.02748:10(3227-3238)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.patcog.2015.02.027
Roli FBiggio BFumera G(2013)Pattern Recognition Systems under AttackProceedings, Part I, of the 18th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Volume 825810.1007/978-3-642-41822-8_1(1-8)Online publication date: 20-Nov-2013
https://dl.acm.org/doi/10.1007/978-3-642-41822-8_1

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