Skip to main content
SpringerLink
Log in

Study and Comparative Analysis of Various Image Spamming Techniques

  • Conference paper
  • First Online:
Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 583))

Abstract

Image spamming is a recently emerging technique that has gained attention because of the filtering problems faced in multimedia data. To compete with the developments in the area of spam filters, spammers started using image spam which resulted in ineffective text analysis of email body. The spam message is embedded into the attached image that is mostly modified randomly to elude signature-based mechanism of detection, and it is made obscure from OCR (Optical Character Recognition) text recognition tools. In this paper, an attempt has been made to study image spam and various image spam filtration techniques. Further, we have discussed different image spam detection techniques applied by researchers along with their pros and cons. Survey suggests that image spam has increased the complexity of spamming. Deployment of spam filtering techniques is mandatory for productivity and integrity of business system. Several approaches followed by research give positive aspect in spamming but there are certain issues with these techniques. Hence, there is a need to have hybrid approach that is cost-effective and feasible.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stuart, I.J., Cha, S., Tappert, C.: A neural network classifier for junk E-mail. Notes Comput. Sci. 3163, 442–450 (2004)

    Article  Google Scholar 

  2. Uemura, M., Tabata, T.: Design and evaluation of a Bayesian-filter-based Image Spam Filtering Method. IEEE (2008)

    Google Scholar 

  3. Patidar, V., Singh, D.: A survey on machine learning methods in spam filtering. Int. J. Res. Comput. Eng. Electron. 2(5) (2013)

    Google Scholar 

  4. Khanum, M.A., Ketari, L.M.: Trends in Combating Image Spam E-mails. Information Technology Department, College of Computer and Information Sciences, King Saud University, Kingdom of Saudi Arabia

    Google Scholar 

  5. Securelist.com: Spam and phishing in Q2 2015. https://securelist.com/analysis/quarterly-spam-reports/71759/spam-and-phishing-in-q2-of-2015/ (2016)

  6. Wang, Z., Josephson, W., Lv, Q., Charikar, M., Li, K.: Filtering image spam with near-duplicate detection. In: 4th Conference Email Anti-spam, CEAS (2007)

    Google Scholar 

  7. Soranamageswari, M., Meena, C.: An efficient feature extraction method for classification of image spam using artificial neural networks. 169–172 (2010)

    Google Scholar 

  8. Duan, Z.H. et al.: Detecting spam zombies by monitoring outgoing messages. IEEE INFOCOM, Rio de Janeiro, 1764–1772 (2009)

    Google Scholar 

  9. Chopra, N.D., Gaikwad, K.P.: Image and text spam mail filtering. Int. J. Comput. Technol. Electron. Eng (IJCTEE). 5(3), Department of Computer Engineering, SND COE and RC Yeola, Nashik, India (2015)

    Google Scholar 

  10. Attar, A., Rad, R.M., Atani, R.E.: A Survey of Image Spamming and Filtering Techniques, pp. 71–105. Springer, Iran (2011)

    Google Scholar 

  11. Liu, Q., Qin, Z., Cheng, H., Wan, M.: Efficient modeling of spam images. In: Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI), pp. 663–666 (2010)

    Google Scholar 

  12. Mehta, B., Nangia, S., Gupta, M., Nejdl, W.: Detecting image spam using visual features and near duplicate detection. In: Proceedings of the 17th International Conference World Wide Web. ACM, pp. 497–506 (2008)

    Google Scholar 

  13. Win, Z.M., Aye, N.: Detecting image spam based on file properties, histogram and hough transform. J. Adv. Comput. Netw. 2(4), 287 (2014)

    Article  Google Scholar 

  14. Kamble, M., Dule, C.: Detecting image spam based on image features using maximum likelihood technique. IJCST, March 2012

    Google Scholar 

  15. Youn, S., Cho. H.: Improved spam filter via handling of text embedded image e-mail. J. Electr. Eng. Technol. 9, 742–748 (2014)

    Google Scholar 

  16. Biggio, B., Fumera, G., Pillai, I., Roli, F.: A survey and experimental evaluation of image spam filtering techniques. Pattern Recognit. Lett. 32, 1436–1446 (2011)

    Article  Google Scholar 

  17. Gupta, A., Singhal, C., Aggarwal, S.: Identification of image spam by using low-level and metadata features. Int. J. Netw. Secur. Appl. 4(2) (2012)

    Google Scholar 

  18. Fumera, G., Pillai, I., Roli, F.: Spam filtering based on the analysis of text information embedded into images. Special issue on Machine Learning in Computer Security, Mach. Learn. Res. 7, 2699–2720 (2006)

    Google Scholar 

  19. Kamble, M., Malik, L.G.: Detecting image spam using principal component analysis and SVM classifier. Int. J. Comput. Sci. Inf. Technol. Secur. 2(6), 1217–1220 (2012)

    Google Scholar 

  20. Security.googleblog.com: Are you a robot? Introducing No CAPTCHA re-CAPTCHA. https://security.googleblog.com/2014/12/are-you-robot-introducing-no-captcha.html (2014)

  21. Arclab.com: Email header: How to read and analyze the email header fields and information about SPF, DKIM, SpamAssasin. https://www.arclab.com/en/kb/email/how-to-read-and-analyze-the-email-header-fields-spf-dkim.html (2011)

  22. Dakhare, B.S., Gaikwad, U.V., Gaikwad, V.B.: Spam detection using email abstraction. Int. J. Innov. Eng. Technol. 3(2), 33–39 (2013)

    Google Scholar 

  23. Wu, C.T., Cheng, K.T., Zhu, Q., Wu, Y.L.: Using visual features for anti-spam filtering. In: Proceedings of the IEEE International Conference on Image Processing, vol. III, pp. 501–504 (2005)

    Google Scholar 

  24. Aradhya, H.B., Myers, G.K., Herson, J.A.: Image analysis for efficient categorization of image-based spam e-mail. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition (ICDAR’2005)

    Google Scholar 

  25. Dredze, M., Gevaryahu, R., Elias-Bachrach, A.: Learning fast classifiers for image spam. CEAS (2007)

    Google Scholar 

  26. Byun, B., Lee, C.H., Webb, S., Pu, C.: A discriminative classifier learning approach to image modeling and spam image identification. In: Proceedings of the Fourth Conference on Email and Anti-Spam (CEAS) (2007)

    Google Scholar 

  27. Gao, Y., Sun, S.B., Qin, K.Y.: A new k-means data clustering approach. J. Comput. Inf. Syst. 4(2), 565–570 (2008)

    Google Scholar 

  28. Wan, M., Zhang, F., Cheng, H., Liu, Q.: Text localization in spam image using edge features. In: Proceedings of International Conference on Communications, Circuits and Systems (ICCCAS), pp. 838–842 (2008)

    Google Scholar 

  29. Sheu, J.J., Chu, K.T.: An efficient spam filtering method by analyzing e-mail’s header session only. Int. J. Innov. Comput. Inf. Control 5(11), 3717–3731 (SCIE), 371129 (2009)

    Google Scholar 

  30. Yin, H., Cheng, F., Zhang, D.: Using LDA and ant colony algorithm for spam mail filtering. In: International Symposium on Information Science and Engineering, IEEE. doi:10.1109/ISISE.2009.82, 368–371 (2009)

  31. Hu, Y., et al.: A scalable intelligent non-content-based spam-filtering framework. Expert Syst. Appl. 37, 8557–8565 (2010)

    Article  Google Scholar 

  32. Cheng, H., Qin, Z., Fu, C., Wang, Y.: A novel spam image filtering framework with multi-label classification. In: ICCCASn, pp. 282–285 (2010)

    Google Scholar 

  33. El-Alfy, E.-S.M., Abdel-Aal, R.E.: Using GMDH-based networks for improved spam detection and email feature analysis. Appl. Soft Comput. 11, 477–488 (2011)

    Article  Google Scholar 

  34. Jitendra, N.S., Bindu, M.H.: E-mail spam filtering using adaptive generic algorithm. Mod. Educ. Comput. Sci. Int. J. Intell. Syst. Appl. 2, 54–60 (2014). doi:10.5815/ijisa.2014.02.07

  35. Khater, I.M., Al-Jarrah, O.M., Al-Duwairi, B.: Hierarchical email spam filtering. Int. J. Comput. Inf. Technol. 5(3), 290–299 (2016)

    Google Scholar 

  36. Deshmukh, H. et al: Spam mail detection using artificial neural networks. Imp. J. Interdiscip. Res. 2(5) (2016). ISSN: 2454-1362, http://www.onlinejournal.in

  37. Khater, I.M., Al-Jarrah, O.M., Al-Duwairi, B.: Hierarchical email spam filtering. Int. of Comput. Inf. Technol. 5(3) (2016). ISSN: 2279-0764

    Google Scholar 

  38. Ghosh, P.: A Framework of email cleansing and mining with case study on image spamming. Inter. J. Adv. Comput. Res. 4(17), 961–965. ISSN (Print): 2249-7277, ISSN (Online): 2277-7970 (2014)

    Google Scholar 

  39. Sheu, J.J.: An efficient two-phase spam filtering method based on e-mails categorization. Int. J. Netw. Secur. 9, 34–43 (2009)

    Google Scholar 

  40. Boscovs.com: 50% off sale. http://www.boscovs.com/shop/dept/50-off-sale/25000000.htm (2016)

  41. Healthymomscircle.com: Work from home jobs for moms. http://healthymomscircle.com/topics/isagenix/work-from-home/ (2014)

  42. Youn, S., Cho, H.: Improved spam filter via handling of text embedded image e-mail. J. Electr. Eng Technol. 9, 742–748 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AIIT, Amity University, Jaipur, Rajasthan, India

    Jasvinder Kumar & Swapnesh Taterh

  2. VIPS, Affiliated to IP University, Delhi, India

    Deepali Kamnthania

Authors
  1. Jasvinder Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swapnesh Taterh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deepali Kamnthania
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jasvinder Kumar .

Editor information

Editors and Affiliations

  1. Department of Applied Science and Engineering, IIT Roorkee, Saharanpur, India

    Millie Pant

  2. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  3. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  4. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  5. Surface Characterization Group, NIMS, Nano Characterization Unit, Advanced Key Technologies Division, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, J., Taterh, S., Kamnthania, D. (2018). Study and Comparative Analysis of Various Image Spamming Techniques. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 583. Springer, Singapore. https://doi.org/10.1007/978-981-10-5687-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-5687-1_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5686-4

  • Online ISBN: 978-981-10-5687-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation