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AI & SOCIETY

, Volume 26, Issue 1, pp 95–101 | Cite as

Machine vision: an aid in reverse Turing test

  • Santosh PutchalaEmail author
  • Nikhil Agarwal
Open Forum

Abstract

Information security is perceived as an important and vital aspect for the survival of any business. Preserving user identity and limiting the access of web resources only to the humans and restricting ‘bots’ is an ever challenging area of study. With the increase in computing power and development of newer approaches towards circumvention and reverse-engineering, the recognition gap present between the machines and the humans is said to be decreasing. Turing test and its modified versions are in place to deal with such problems and ways to resolve them by developing complex algorithms for bot prevention systems like CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). This paper will deal with the use of “Machine Vision” for judging the ability of the machines to compete with humans in breaking sequences of security systems like CAPTCHA. Reverse Turing test will be put to practise here. Complex image recognition technologies and novel approaches towards using Human interactive proofs (HIP) are discussed. The progress of Turing test over the past 60 years has been paid due attention at the end. After all this experimentation, it can be said that the current machine vision is quite poor and is far worse than it is expected to be.

Keywords

Machine Vision Optical Character Recognition Banding Noise Joint Photographic Expert Group Turing Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Antonatos EA (2006) Enhanced CAPTCHAs: using animation to tell humans and computers apart. Int Fed Inform Process 97–108Google Scholar
  2. Baird HS, Popat K (2002) Human interactive proofs and document image analysis. In: Document analysis systems V-lecture notes in computer science (pp. 531–537). Springer, BerlinGoogle Scholar
  3. Baird HS, Coates AL, Fateman RJ (2001) PessimalPrint: a reverse Turing test. In: Proceedings of the 6th international conference on document analysis and recognition, 1154–1158Google Scholar
  4. Baird HS, Moll MA, Wang S-Y (2005) ScatterType: a legible but hard-to-segment CAPTCHA. In: Proceedings of the 2005 eighth international conference on document analysis and recognition (ICDAR’05). IEEE Computer SocietyGoogle Scholar
  5. Chanathip N, Matthew D (2004) Mitigating dictionary attacks with text-graphics character CAPTCHAs. In: TENCON 2004 conference proceedings 2004Google Scholar
  6. Chellapilla K, Simard P (2004) Using machine learning to break visual human interaction proofs (HIPs). Neural information processing systems (NIPS’2004). MIT Press, CambridgeGoogle Scholar
  7. Goodman J, Rounthwaite R (2004). Stopping outgoing spam. In: Proceedings of the 5th ACM conference on electronic commerce. New YorkGoogle Scholar
  8. Lillibridge MD, Adabi M, Bharat K, Broder A (2001) Patent No. US Patent 6,195,698. United StatesGoogle Scholar
  9. Mori G, Malik J (2003) Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. Proc Comp Vis Pattern Rec (CVPR) Conf IEEE Comput Soc 1:I-134–I-141Google Scholar
  10. Nagy G (1996) Modern optical character recognition. The Froehlich/Kent Encycl Telecommun 11:473–531Google Scholar
  11. Pavlidis T (2000) Thirty years at the pattern recognition front. King-Sun Fu prize lecture 11th ICPR. Barcelona, SpainGoogle Scholar
  12. Rice SV, Jenkins FR, Nartker TA (1996) The fifth annual test of OCR accuracy. ISRI TR-96-01, Las VegasGoogle Scholar
  13. Rice SV, Nagy G, Nartker TA (1999) OCR: an illustrated guide to the frontier. Kluwer, AmsterdamGoogle Scholar
  14. Turing A (1950) Computing machinery and intelligence. Mind 59(236):433–460CrossRefMathSciNetGoogle Scholar
  15. Von Ahn L, Blum M, Langford J (2000) Completely automatic public Turing test to tell computers and humans apart. The CAPTCHA Project, www.captcha.net, Dept. of Computer Science, Carnegie-Mellon Univ
  16. Von Ahn L, Blum M, Langford J (2008) reCAPTCHA: human-based character recognition via web security measures. Science 321:1465–1486CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Europe Asia Business SchoolPuneIndia

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