Image Understanding for Automatic Human and Machine Separation.
C.R. Macias · 2013
The research presented in this thesis aims to extend the capabilities of human interaction proofs in order to improve security in web applications and ser-vices. The research focuses on developing a more robust and efficient Com-pletely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) to increase the gap between human recognition and machine recognition. Two main novel approaches are presented, each one of them tar-geting a different area of human and machine recognition: a character recog-nition test, and an image recognition test. Along with the novel approaches, a categorisation for the available CAPTCHA methods is also introduced. The character recognition CAPTCHA is based on the creation of depth perception by using shadows to represent characters. The characters are cre-ated by the imaginary shadows produced by a light source, using as a basis the gestalt principle that human beings can perceive whole forms instead of just a collection of simple lines and curves. This approach was developed in two