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Reseach Article

Enhanced ReP-ETD Anti-Spamming Technique

by Himanshu Bagwaiya, Varsha Sharma, Sanjeev Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 1
Year of Publication: 2016
Authors: Himanshu Bagwaiya, Varsha Sharma, Sanjeev Sharma
10.5120/ijca2016908772

Himanshu Bagwaiya, Varsha Sharma, Sanjeev Sharma . Enhanced ReP-ETD Anti-Spamming Technique. International Journal of Computer Applications. 143, 1 ( Jun 2016), 11-14. DOI=10.5120/ijca2016908772

@article{ 10.5120/ijca2016908772,
author = { Himanshu Bagwaiya, Varsha Sharma, Sanjeev Sharma },
title = { Enhanced ReP-ETD Anti-Spamming Technique },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 1 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number1/25040-2016908772/ },
doi = { 10.5120/ijca2016908772 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:45:10.468352+05:30
%A Himanshu Bagwaiya
%A Varsha Sharma
%A Sanjeev Sharma
%T Enhanced ReP-ETD Anti-Spamming Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 1
%P 11-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet is a widely used paradigm where sharing of multimedia content is a major task. Spam image (an image that contains obscure or irrelevant content) is often discovered in web data available in servers and worldwide search engines. Techniques for spam filtering and finding or detecting obscure content in multimedia data (such as .JPEG, .png format of images) are available in the literature. This paper reviews different existing techniques to deal with obscure images and presents an enhanced ReP-ETD (Repetitive Pre-processing technique for Embedded Text Detection) technique in order to detect the obscured content in image data. The technique proposed in this paper first pre-process the multimedia image data using a Linux image script and further on OCR (Optical Character Reader) is used for the spamming image detection and depth analysis. The main contribution of this paper is to discover and perform spam word extraction from the embedded obscured image.

References
  1. Wikipedia,“Spam”http://en.wikipedia.org/wiki/Spam_(electronics)
  2. Wikipedia,“Emailspam”http://en.wikipedia.org/wiki/Emailspam
  3. Hope, P., Bowling, J. R., and Liszka, K. J., “Artificial Neural Networks as a Tool for Identifying Image Spam”, The 2009 International Conference on Security and Management (SAM'09), pp. 447-451, July 2009
  4. Battista Biggio, Giorgio Fumera, Ignazio Pillai, Fabio Roli, "A survey and experimental evaluation of image spam filtering techniques, Pattern Recognition Letters". Volume 32, Issue 10, pp 1436-1446, ISSN 0167-8655, 15 July 2011
  5. Bob West, "Getting it Wrong: Corporate America Spams the Afterlife", Clueless Mailers. (January 19, 2008) Retrieved 2010-0923
  6. Fumera, Giorgio, Ignazio Pillai, and Fabio Roli. "Spam filtering based on the analysis of text information embedded into images", The Journal of Machine Learning Research vol.7, pp. 2699-2720, 12/2006.
  7. Symantec.com, "symantec.com", Retrieved 2012-12-10.
  8. Krasser, S.; Yuchun Tang; Gould, J.; Alperovitch, D.; Judge, P.; "Identifying Image Spam based on Header and File Properties using C4.5 Decision Trees and Support Vector Machine Learning," Information Assurance and Security Workshop, 2007. IAW '07. IEEE SMC, vol., no., pp.255-261, 20-22 June 2007.
  9. Aradhye, Hrishikesh B., Gregory K. Myers and James A. Herson “Image analysis for efficient categorization of image-based spam email.” In Document analysis and Recognition, 2005. Proceeding. Eighth International conference on, IEEE pp. 914-918, August 2005
  10. Mark Dredze, Reuven Gevarvahu and Ari Elias-Bachrach, “Lerning fast classifers for Image Spam”, CEAS 2007
  11. Asha S Manek, Shamini D.k, Bhat and Shenoye” ReP-ETD: A Repetitive Preprocessing Technique for Embedded Text Detection from Images in Spam Emails”978-1-4799-2572-8/14/$31.00c@2014 IEEE
  12. Hope, P., Bowling, J. R., and Liszka, K. J., Artificial Neural Networks as a Tool for Identifying Image Spam, The 2009 International Conference on Security and Management (SAM'09), July 2009, pp. 447-451.
  13. Chao Wang, Fengli Zhang, Fagen Li, Qiao Liu, "Image spam classification based on low-level image features", Communications, Circuits and Systems (ICCCAS), 2010 International Conference on, pp.290-293, 28-30 July 2010.
  14. Yan Gao, Ming Yang, and Alok Choudhary, “Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach”, In Advanced Data Mining and Applications, pp. 152-164. Springer Berlin Heidelberg, 2009.
  15. Giorgio Fumera, Ignazio Pillai and Fabio Roli, “Spam Filtering Based On The Analysis Of Text Information Embedded Into Images”, Journal of Machine Learning Research 7 (2006) 26992720, Submitted 3/06; Revised 9/06; Published 12/06
  16. Congfu Xu, Kevin Chiew, Yafang Chen and Juxin Liu, “Fusion of Text and Image Features: A New Approach to Image Spam Filtering”, Y.Wang and T. Li (Eds.): Practical Applications of Intelligent Systems, AISC 124, pp. 129–140. springerlink.com Springer-Verlag Berlin Heidelberg 2011.
  17. Basheer Al-Duwairi, Ismail Khater and Omar Al-Jarrah, “Detecting Image Spam Using Image Texture Features”, International Journal for Information Security Research (IJISR), Volume2, Issues3/4,pp344353,September/December 2012.
Index Terms

Computer Science
Information Sciences

Keywords

K OCR obscure images CAPTCH