On random field CAPTCHA generation

• Author(s) / Creator(s)

  • Kouritzin, Michael
  • Wu, B.
  • Newton, F.

• Herein, we propose generating CAPTCHAs through random field simulation and give a novel, effective and efficient algorithm to do so. Indeed, we demonstrate that sufficient information about word tests for easy human recognition is contained in the site marginal probabilities and the site-to-nearby-site covariances and these quantities can be embedded into KNW conditional proba- bilities, designed for effective simulation. The CAPTCHAs are then partial random realizations of the random CAPTCHA word: we start with an initial random field (e.g., randomly scattered letter pieces) and use Gibbs resampling to re-simulate portions of the field repeatedly using the KNW conditional probabilities until the word becomes human-readable. The residual randomness from the initial random field together with the random implementation of the CAPTCHA word provide significant resistance to attack. This results in a CAPTCHA which is unrecognizable to modern OCR but is recognized about 95% of the time in a human readability study.

• Date created

  2013

• Subjects / Keywords

  • Image processing
  • Statistical information compression
  • Security
  • Simulations
  • Markov random field

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/R3028PD1Q

• License

  © 2013 IEEE Transactions on Image Processing. This version of this article is open access and can be downloaded and shared. The original author(s) and source must be cited.