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Machine vision: an aid in reverse Turing test

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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.

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Notes

  1. Bots are software applications that run automated tasks over the Internet at a much higher rate than would be possible for a human alone.

  2. Joint photographic experts group (JPEG), Graphics interchange format (GIF) and Portable network graphics (PNG) are digital image formats.

  3. GIMPY is a reliable system. It was originally built for Yahoo! to keep bots out of their chat rooms, to prevent scripts from obtaining an excessive number of their e-mail addresses, and to prevent computer programs from publishing classified ads. It is not an acronym.

  4. The reduction in the inherent optimum detail and reduction in quality caused by unavoidable factors not associated with the sensor system lead to image degradation. Image distortion is the alteration of the original shape (or other characteristic) of detail in the image or the entire image.

  5. Set of human information or machine data can be considered a signal.

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Authors and Affiliations

  1. Europe Asia Business School, Pune, India

    Santosh Putchala & Nikhil Agarwal

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  1. Santosh Putchala
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  2. Nikhil Agarwal
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Correspondence to Santosh Putchala.

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Putchala, S., Agarwal, N. Machine vision: an aid in reverse Turing test. AI & Soc 26, 95–101 (2011). https://doi.org/10.1007/s00146-009-0231-4

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  • DOI: https://doi.org/10.1007/s00146-009-0231-4

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