CFP last date
20 January 2025
Reseach Article

A Simple Approach to Automatic Filling CAPTCHA using Pattern Recognition

by Ademir B. Santos Neto, Maria Da C. M. Batista, Tiago A. E. Ferreira
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 2
Year of Publication: 2017
Authors: Ademir B. Santos Neto, Maria Da C. M. Batista, Tiago A. E. Ferreira
10.5120/ijca2017914669

Ademir B. Santos Neto, Maria Da C. M. Batista, Tiago A. E. Ferreira . A Simple Approach to Automatic Filling CAPTCHA using Pattern Recognition. International Journal of Computer Applications. 170, 2 ( Jul 2017), 1-7. DOI=10.5120/ijca2017914669

@article{ 10.5120/ijca2017914669,
author = { Ademir B. Santos Neto, Maria Da C. M. Batista, Tiago A. E. Ferreira },
title = { A Simple Approach to Automatic Filling CAPTCHA using Pattern Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 2 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number2/28039-2017914669/ },
doi = { 10.5120/ijca2017914669 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:22.248533+05:30
%A Ademir B. Santos Neto
%A Maria Da C. M. Batista
%A Tiago A. E. Ferreira
%T A Simple Approach to Automatic Filling CAPTCHA using Pattern Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 2
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article shows an easy and simple approach to recognize characters in CAPTCHA images, where the k-NN (k nearest neighbor) algorithm is employed. This proposal to recognize characters in CAPTCHA images has the objective of autofill these components in order to support automation of access to systems. The main aim of this article is to show the steps involved in the proposed process about automatic filling CAPTCHAs since the image’s handling until the classification of the characters through a simple and low-cost (implementation) technique of pattern recognition. Experimental results and an error distribution about the characters’ classification are showed, where it is demonstrated the possibility of application in real cases of the proposal presented.

References
  1. Karmand Abdalla and Mehmet Kaya. An evaluation of different types of captcha: Effectiveness, user-friendliness and limitations. International Journal of Scientific Research in Information Systems and Engineering (IJSRISE), 2(3), 2017.
  2. Elie Bursztein, Matthieu Martin, and John Mitchell. Textbased captcha strengths and weaknesses. In Proceedings of the 18th ACM conference on Computer and communications security, pages 125–138. ACM, 2011.
  3. Kumar Chellapilla, Kevin Larson, Patrice Y Simard, and Mary Czerwinski. Building segmentation based humanfriendly human interaction proofs (hips). In 2nd International Workshop on Human Interactive Proofs, pages 1–26. Springer, 2005.
  4. Richard O Duda, Peter E Hart, and David G Stork. Pattern classification. John Wiley & Sons, 2012.
  5. Jeremy Elson, John R Douceur, Jon Howell, and Jared Saul. Asirra: a captcha that exploits interest-aligned manual image categorization. In ACM Conference on Computer and Communications Security, volume 7, pages 366–374, 2007.
  6. Haichang Gao, Jeff Yan, Fang Cao, Zhengya Zhang, Lei Lei, Mengyun Tang, Ping Zhang, Xin Zhou, Xuqin Wang, and Jiawei Li. A simple generic attack on text captchas. In Network and Distributed System Security Symposium (NDSS), San Diego, USA, 2016.
  7. Philippe Golle. Machine learning attacks against the asirra captcha. In Proceedings of the 15th ACM conference on Computer and communications security, pages 535–542. ACM, 2008.
  8. Rafaqat Hussain, Hui Gao, and Riaz Ahmed Shaikh. Segmentation of connected characters in text-based captchas for intelligent character recognition. Multimedia Tools and Applications, pages 1–15, 2016.
  9. Rafaqat Hussain, Kamlesh Kumar, Hui Gao, and Imran Khan. Recognition of merged characters in text based captchas. In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on, pages 3917– 3921. IEEE, 2016.
  10. H. KardanMoghaddam. Proposing an algorithm for converting published and handwritten texts to captcha by using image processing. In 2016 Eighth International Conference on Information and Knowledge Technology (IKT), pages 170–176, Sept 2016.
  11. R Muralidharan and C Chandrasekar. Object recognition using svm-knn based on geometric moment invariant. International Journal of Computer Trends and Technology-July to Aug, (2011):215–220, 2011.
  12. Moni Naor. Verification of a human in the loop or identification via the turing test. Unpublished draft from http://www. wisdom. weizmann. ac. il/˜ naor/PAPERS/human abs. html, 1996.
  13. Sonal Paliwal, Rajesh Shyam Singh, and Mandoria H. L. A survey on various text detection and extraction techniques from videos and images. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), 6:1–10, 2016.
  14. V Premanand, A Meiappane, and V Arulalan V Arulalan. Survey on captcha and its techniques for bot protection. International Journal of Computer Applications, 109(5):1–4, 2015.
  15. Ved Prakash Singh and Preet Pal. Survey of different types of captcha. International Journal of Computer Science and Information Technologies, 5(2):2242–2245, 2014.
  16. S. Sivakorn, I. Polakis, and A. D. Keromytis. I am robot: (deep) learning to break semantic image captchas. In 2016 IEEE European Symposium on Security and Privacy (EuroS& P), pages 388–403, March 2016.
  17. Luis Von Ahn, Manuel Blum, Nicholas J Hopper, and John Langford. Captcha: Using hard ai problems for security. In International Conference on the Theory and Applications of Cryptographic Techniques, pages 294–311. Springer, 2003.
  18. Jeff Yan and Ahmad Salah El Ahmad. Breaking visual captchas with naive pattern recognition algorithms. In Computer Security Applications Conference, 2007. ACSAC 2007. Twenty-Third Annual, pages 279–291. IEEE, 2007.
  19. Jeff Yan and Ahmad Salah El Ahmad. A low-cost attack on a microsoft captcha. In Proceedings of the 15th ACM conference on Computer and communications security, pages 543– 554. ACM, 2008.
  20. Hao Zhang, Alexander C Berg, Michael Maire, and Jitendra Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), volume 2, pages 2126–2136. IEEE, 2006.
  21. Wolfhart Zimmermann. The power counting theorem for minkowski metric. Communications in Mathematical Physics, 11(1):1–8, 1968.
Index Terms

Computer Science
Information Sciences

Keywords

CAPTCHA pattern recognition classification automation character recognition