Image Understanding for Automatic Human and Machine Separation.

Abstract

The research presented in this thesis aims to extend the capabilities of human interaction proofs in order to improve security in web applications and services. The research focuses on developing a more robust and efficient Completely 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 targeting a different area of human and machine recognition: a character recognition 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 created 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 stages: firstly, two dimensional characters, and secondly three-dimensional character models. The image recognition CAPTCHA is based on the creation of cartoons out of faces. The faces used belong to people in the entertainment business, politicians, and sportsmen. The principal basis of this approach is that face perception is a cognitive process that humans perform easily and with a high rate of success. The process involves the use of face morphing techniques to distort the faces into cartoons, allowing the resulting image to be more robust against machine recognition. Exhaustive tests on both approaches using OCR software, SIFT image recognition, and face recognition software show an improvement in human recognition rate, whilst preventing robots break through the tests.