HuMan: an accessible, polymorphic and personalized CAPTCHA interface with preemption feature tailored for persons with visual impairments

• Published in: Universal access in the information society 2018-11, Vol.17 (4), p.841-864
• Main Authors: Kuppusamy, K. S., Aghila, G.
• Format: Article
• Language: English
• Subjects: Accessibility; Algorithms; Automation; Blindness; Computation; Computer Communication Networks; Computer Science; Computers and Society; Customization; Disability; Handicapped accessibility; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Interfaces; IT in Business; Logic; Long Paper; People with disabilities; Preemption; Security aspects; Success; Turing machines; Usability; User Interfaces and Human Computer Interaction; Visual impairment; Visual perception; Webs; World Wide Web
• ISSN: 1615-5289; 1615-5297
• DOI: 10.1007/s10209-017-0567-3

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is one of the major security components in the provision of fair web access by differentiating human access from malicious, automated access by bots. Though the CAPTCHA strengthens the security aspect of web access, their accessibility to people with visual impairments has inherent unresolved challenges. This paper presents an accessible CAPTCHA model termed HuMan (human or machine?) which aims at providing an audio-based CAPTCHA for people with visual impairments. The HuMan model incorporates personalization into the CAPTCHA access. The polymorphic nature of resolving the HuMan CAPTCHA facilitates kaleidoscopic behavior in CAPTCHA rendering. The presence of ambient noise and requirement of common sense knowledge to answer the questions presented by HuMan CAPTCHA model makes it friendlier toward human users. The HuMan model has a CAPTCHA preemption feature which enables the user to stop the challenge audio as soon as the answer is identified. The results of experiments conducted on the prototype implementation of HuMan model project the mean success rate of 92.46 % and system usability scale score of 82.44 for persons with visual impairments and 82.63 for sighted users.