• DocumentCode
    1760434
  • Title

    fgCAPTCHA: Genetically Optimized Face Image CAPTCHA 5

  • Author

    Powell, Brian M. ; Goswami, Gaurav ; Vatsa, Mayank ; Singh, Rajdeep ; Noore, Afzel

  • Author_Institution
    Lane Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV, USA
  • Volume
    2
  • fYear
    2014
  • fDate
    2014
  • Firstpage
    473
  • Lastpage
    484
  • Abstract
    The increasing use of smartphones, tablets, and other mobile devices poses a significant challenge in providing effective online security. CAPTCHAs, tests for distinguishing human and computer users, have traditionally been popular; however, they face particular difficulties in a modern mobile environment because most of them rely on keyboard input and have language dependencies. This paper proposes a novel image-based CAPTCHA that combines the touch-based input methods favored by mobile devices with genetically optimized face detection tests to provide a solution that is simple for humans to solve, ready for worldwide use, and provides a high level of security by being resilient to automated computer attacks. In extensive testing involving over 2600 users and 40000 CAPTCHA tests, fgCAPTCHA demonstrates a very high human success rate while ensuring a 0% attack rate using three well-known face detection algorithms.
  • Keywords
    face recognition; mobile computing; security of data; automated computer attacks; face detection algorithms; fgCAPTCHA; genetically optimized face image CAPTCHA; modern mobile environment; novel image-based CAPTCHA; online security; touch-based input methods; CAPTCHAs; Face detection; Face recognition; Mobile communication; Mobile handsets; Noise measurement; Security; CAPTCHA; Mobile security; face detection; web security;
  • fLanguage
    English
  • Journal_Title
    Access, IEEE
  • Publisher
    ieee
  • ISSN
    2169-3536
  • Type

    jour

  • DOI
    10.1109/ACCESS.2014.2321001
  • Filename
    6807630