On Random Field CAPTCHA Generation

  • Author / Creator
    Newton, Fraser
  • In this thesis, we develop a novel method of generating CAPTCHAs, which are used to protect online resources from abuse by computer agents. We view CAPTCHA generation as random field simulation and construct a CAPTCHA by evolving an initial state via resimulating pixels until the image becomes readable. We empirically demonstrate that this CAPTCHA is easy for humans to read but difficult for computer programs to crack. We describe how to develop variants of this CAPTCHA; in particular, we implement and assess the utility of a grey-level variant. We establish a method of maximizing the effectiveness of a CAPTCHA variant, and perform analysis to determine which properties of the CAPTCHA most effectively differentiate humans and computer programs. We extend the random field used in the CAPTCHA application to multiple dimensions in the context of graph theory, and describe the generic method of applying the random field to suitable problems.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Statistics
  • Supervisor / co-supervisor and their department(s)
    • Kouritzin, Michael (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Hillen, Thomas (Mathematical and Statistical Sciences)
    • Schmuland, Byron (Mathematical and Statistical Sciences)
    • Chough, Keumhee Carrière (Mathematical and Statistical Sciences)