CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Design and Comparison of Advanced Color based Image CAPTCHAs

by Mandeep Kumar, Renu Dhir
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 15
Year of Publication: 2013
Authors: Mandeep Kumar, Renu Dhir
10.5120/10005-4870

Mandeep Kumar, Renu Dhir . Design and Comparison of Advanced Color based Image CAPTCHAs. International Journal of Computer Applications. 61, 15 ( January 2013), 24-29. DOI=10.5120/10005-4870

@article{ 10.5120/10005-4870,
author = { Mandeep Kumar, Renu Dhir },
title = { Design and Comparison of Advanced Color based Image CAPTCHAs },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 15 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number15/10005-4870/ },
doi = { 10.5120/10005-4870 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:09:22.746410+05:30
%A Mandeep Kumar
%A Renu Dhir
%T Design and Comparison of Advanced Color based Image CAPTCHAs
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 15
%P 24-29
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

CAPTCHA is a technology which has its base in a test called the Turing Test. Alan Turing, proposed this test as a way to examine whether or not machines can think or appear to think like humans. The main purpose of a CAPTCHA is to block form submissions from spam bots- that is automated scripts. Various types of CAPTCHAs are used, which mostly requires users to enter the strings of characters that appear in distorted form on the screen. These types of distorted stings are unable to understand by bots but human can. The CAPTCHA types are either text based or image based. In this paper, a new color based CAPTCHA is described, which provides color based images to human and human will answer to interrogator with color name or so on the question asked during turing test. These colored images can have single color image, more than one color image or it can have images with objects (like monitor, car, flower etc). For these types of questions, the computer machine will be unable to answer and it means unable to break CAPTCHA. This paper describes in detail the proposed CAPTCHA technology principle, method of implementation, variations and comparison of the accuracy rates. We conducted various experiments to measure the viability and usability of this CAPTCHA approach. An accuracy of 100%, 95% and 90% is observed with single color, multi color and color image based CAPTCHAs respectively.

References
  1. Ahn, L. von, Blum, M. , Hopper, N. J. , & Langford, J. 2003. CAPTCHA: Using hard AI problems for security
  2. Ibrahim Furkan Ince, Ilker Yengin, Yucel Batu Salman, Hwan-Gue Cho, Tae-Cheon Yang1 2008. Designing CAPTCHA Algorithm: Splitting and Rotating the Images against OCRs -Third International Conference on Convergence and Hybrid Information Technology.
  3. H. Baird and K. Popat, 2002. Human Interactive Proofs and Document Image Analysis, In Proceedings of the 5th IAPR International Workshop on Document Analysis Systems , Princeton, NJ, USA
  4. Ahmedy, I. ; Portmann, M. ; 2010. Using Captchas to Mitigate the VoIP Spam Problem; Second International Conference on Computer Research and Development.
  5. M. Blum, L. A. von Ahn, and J. Langford, Nov. 2000 , The CAPTCHA Project, Completely Automatic Public Turing Test to Tell Computers and Humans Apart, http://www. captcha. net.
  6. L. von Ahn, M. Blum, and J. Langford, Feb. 2004, Telling Humans and Computers Apart Automatically, Communications of the ACM.
  7. S. Shirali-Shahreza and A. Movaghar. 2007. A New Anti-Spam Protocol Using CAPTCHA. In Proceedings of IEEE International Conference on Networking, Sensing and Control.
  8. R. Dhamija and J. D. Tygar. 2005. Phish and HIPs: Human Interactive Proofs to Detect Phishing Attacks. In Proceedings of the 2nd International Workshop on Human Interactive Proofs.
  9. J. P. Bigham, and A. C. Cavender, 2009. Evaluating existing audio CAPTCHAs and an interface optimized for non-visual use. In Proceedings of ACM.
  10. Bursztein, Elie; Bethard, Steven; Fabry, Celine; Mitchell, John C. ; Jurafsky, Dan; 2010. How Good Are Humans at Solving CAPTCHAs? A Large Scale Evaluation; IEEE Symposium on Security and Privacy (SP).
  11. A. L. Coates, R. J. Fateman, and H. S. Baird. 2001 Pessimal Print: A Reverse Turing Test, In Proceeding of the 6th Inernational Conference on Document Analysis and Recognition, Seattle, USA.
  12. M. Chew and H. S. Baird, BaffleText: a Human Interactive Proof, Proc. , 2003. 10th SPIE/IS&T Document Recognition and Retrieval Conf. (DRR2003), Santa Clara, CA
  13. L. Von Ahn, M. Blum, and J. Langford, 2004. Telling Human and Computers Apart Automatically, Communications of the ACM
  14. J. Elson, J. R. Douceur, J. Howell, and J. Saul, 2007. Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In Proceedings of the 14th ACM Conference on Computer and Communications Security, Alexandria, Virginia, USA.
  15. G. Sauer, H. Hochheiser, J. Feng, and J. Lazar. 2008 Towards a Universally Usable CAPTCHA. In Proceedings of the Symposium on Accessible Privacy and Security, ACM Symposium On Usable Privacy and Security (SOUPS'08), Pittsburgh, PA, USA.
  16. G. Mori and J. Malik. June 2003. Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition
  17. K. Chellapilla, K. Larson, P. Simard, and M. Czerwinski. Apr. 2005. Designing human friendly human interaction proofs (HIPs). Proc. of SIGCHI Conf. on Human factors in computing systems.
  18. S. Y. Huang, Y. K. Lee, G. Bell, and Z. Ou. 2008. A Projection-based Segmentation Algorithm for Breaking MSN and YAHOO CAPTCHAs, Proceedings of the World Congress on Engineering,UK
  19. K. Chellapilla, and P. Simard. Vol. 17. Using Machine Learning to Break Visual Human Interaction Proofs (HIPs), Advances in Neural Information Processing Systems, , MIT Press.
  20. Elham Afsari Yeganeh, 2009. How CAPTCHA annoys genuine users. Anti-Spam Research Laboratory, Digital Ecosystem and Business Intelligence Institute, CUT.
  21. Donn Morrison, Stéphane Marchand-Maillet, Éric Bruno. June 2009. TagCaptcha: annotating images with CAPTCHAs. Proceedings of the ACM SIGKDD Workshop on Human Computation.
  22. Chandavale, A. A. ; Sapkal, A. M. ; Jalnekar, R. M. Dec 2009. Algorithm to Break Visual CAPTCHA. ICETET, 2nd International Conference.
  23. Jain, Ashish; Raj, A. ; Pahwa, T. ; Jain, Abhimanyu; Gupta, A. July 2009. Overlapping variants of sequenced tagged captcha (STC): Generation and their comparative analysis. Networked Digital Technologies. First International Conference.
  24. Ritendra Datta, Jia Li, James Z. Wang. 2009. Exploiting the human-machine gap in image recognition for designing CAPTCHAs. IEEE Transactions on Information Forensics and Security.
  25. Yan, J. ; El Ahmad, A. S. 2009 . Security & Privacy, IEEE Volume 7, Issue 4. CAPTCHA Security: A Case Study.
  26. Rich Gossweiler, Maryam Kamvar, Shumeet Baluja. 2009. What's up CAPTCHA?: a CAPTCHA based on image orientation. Proceedings of the 18th international conference on World Wide Web.
  27. Paul Festa. 2003. Spam-bot tests flunk the blind, CNET News. com, Available at http://www. news. com/2100-1032-1022814. html.
  28. A. Gupta, A. Jain and A. Raj, 2009 . sequenced tagged Captcha: generation and its analysis. IEEE International Advance Computing Conference.
  29. J. P. Bigham, and A. C. Cavender, 2009. Evaluating existing audio CAPTCHAs and an interface optimized for non-visual use. In Proceedings of ACM CHI 2009 onference on Human Factors in Computing Systems.
  30. Greg Mori and Jitendra Malik. 2003 Recognising Objects in Adversarial Clutter: Breaking a Visual CAPTCHA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR'03).
  31. J Yan and A S El Ahmad. 2007 Breaking Visual CAPTCHAs with Naïve Pattern Recognition Algorithms, in Proc. of the 23rd Annual Computer Security Applications Conference (ACSAC'07). FL, USA, Dec 2007. IEEE computer society.
  32. J Yan and A S El Ahmad. 2008. A Low-cost Attack on a Microsoft CAPTCHA, School of Computing Science Technical Report, Newcastle University, England.
  33. Yan and A S El Ahmad. 2008. Is cheap labour behind the scene? - Low-cost automated attacks on Yahoo CAPTCHAs , School of Computing Science Technical Report, Newcastle University, England.
  34. HS Baird, MA Moll and SY Wang. 2005. ScatterType: A Legible but Hard-to-Segment CAPTCHA, Eighth International Conference on Document Analysis and Recognition.
  35. P Simard, R Szeliski, J Benaloh, J Couvreur and I Calinov, 2003. Using Character Recognition and Segmentation to Tell Computers from Humans , Int'l Conference on Document Analysis and Recogntion (ICDAR).
  36. C Pope and K Kaur. 2005. Is It Human or Computer? Defending E-Commerce with CAPTCHA, IEEE IT Professional.
  37. Penn Wu and Pedro Manrique 2012, Teaching CAPTCHA in A PHP Programming Course, Copyright by CCSC.
Index Terms

Computer Science
Information Sciences

Keywords

CAPTCHA Image Processing Spam Automated Attacks Character Recognition Usability Automated program

Powered by PhDFocusTM