A case study of the robustness and the usability of CAPTCHA

The websites and network application experienced explosive growth in the past two decades. As the evolution of smartphones and mobile communication network have evolved, smart phone s user experience has been improved to a high level, and more and more people prefer to use smartphones. However, the development of techniques will not only increase the users experience but also bring threats of cracking. The development of techniques brought the potential threats to websites security. As a result, CAPTCHA, Completely Automated Public Turing test to tell Computers and Humans Apart, forms one of the methods to impede spamming attacks. As CAPTCHA s definition indicates, CAPTCHA should be recognized by humans easily while shouldn t be recognized computers. These two attributes of CAPTCHA can be considered as usability and robustness. Some CAPTCHA is difficult to be recognized by computers, but humans may also find difficult to recognize it. Therefore, the purpose of the thesis is to find out the balance between usability and robustness of CAPTCHA. Therefore, the related researches about the usability and the robustness of CAPTCHA will be reviewed, and the process of automatic CAPTCHA recognition will be Figured out and implemented by the author. The implementation will be based on the existed algorithms and a case study. The findings are the factors for improving CAPTCHA s robustness. They are from the each step of a specific process of automatic CAPTCHA recognition. Then the factors will be compared with the issues which are from the related usability research. The discussion will derive some possible ways, such as adding confusing characters and increasing data s diversity to improve robustness while keeping the usability according to the derived factors

Self Designing Pattern Recognition System Employing Multistage Classification

Recently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distortion. Some classification techniques that are introduced in the literature are described in Chapter one. In this dissertation, a pattern recognition approach that can be designed to have evolutionary learning by developing the features and selecting the criteria that are best suited for the recognition problem under consideration is proposed. Chapter two presents some of the features used in developing the set of criteria employed by the system to recognize different types of signals. It also presents some of the preprocessing techniques used by the system. The system operates in two modes, namely, the learning (training) mode, and the running mode. In the learning mode, the original and preprocessed signals are projected into different transform domains. The technique automatically tests many criteria over the range of parameters for each criterion. A large number of criteria are developed from the features extracted from these domains. The optimum set of criteria, satisfying specific conditions, is selected. This set of criteria is employed by the system to recognize the original or noisy signals in the running mode. The modes of operation and the classification structures employed by the system are described in details in Chapter three. The proposed pattern recognition system is capable of recognizing an enormously large number of patterns by virtue of the fact that it analyzes the signal in different domains and explores the distinguishing characteristics in each of these domains. In other words, this approach uses available information and extracts more characteristics from the signals, for classification purposes, by projecting the signal in different domains. Some experimental results are given in Chapter four showing the effect of using mathematical transforms in conjunction with preprocessing techniques on the classification accuracy. A comparison between some of the classification approaches, in terms of classification rate in case of distortion, is also given. A sample of experimental implementations is presented in chapter 5 and chapter 6 to illustrate the performance of the proposed pattern recognition system. Preliminary results given confirm the superior performance of the proposed technique relative to the single transform neural network and multi-input neural network approaches for image classification in the presence of additive noise