Building Emerging Images with Tiled Orders and Posets

We introduce an algorithm based on posets and tiled orders to generate emerging images. Experimental results allow concluding that images obtained with these kinds of tools are easy to detect by human beings. It is worth pointing out that the emergence phenomenon is a Gestalt grouping law associated with AI open problems. For this reason, emerging images have arisen in the last few years as a tool in the context of the development of human interactive proofs.

Proposing a Scheme for Human Interactive Proof Test using Plasma Effect

Human Interactive Proofs (HIPs) are automatic inverse Turing tests, which are intended to differentiate between people and malicious computer programs. The mission of making good HIP system is a challenging issue, since the resultant HIP must be secure against attacks and in the same time it must be practical for humans. Text-based HIPs is one of the most popular HIPs types. It exploits the capability of humans to recite text images more than Optical Character Recognition (OCR), but the current text-based HIPs are not well-matched with rapid development of computer vision techniques, since they are either vey simply passed or very hard to resolve, thus this motivate that continuous efforts are required to improve the development of HIPs base text. In this paper, a new proposed scheme is designed for animated text-based HIP; this scheme exploits the gap between the usual perception of human and the ability of computer to mimic this perception and to achieve more secured and more human usable HIP. This scheme could prevent attacks since it's hard for the machine to distinguish characters with animation environment displayed by digital video, but it's certainly still easy and practical to be used by humans because humans are attuned to perceiving motion easily. The proposed scheme has been tested by many Optical Character Recognition applications, and it overtakes all these tests successfully and it achieves a high usability rate of 95%.

Proposing a Scheme for Human Interactive Proof Test sing Plasma Effect

Human Interactive Proofs (HIPs) are automatic inverse Turing tests, which are intended to differentiate between people and malicious computer programs. The mission of making good HIP system is a challenging issue, since the resultant HIP must be secure against attacks and in the same time it must be practical for humans. Text-based HIPs is one of the most popular HIPs types. It exploits the capability of humans to recite text images more than Optical Character Recognition (OCR), but the current text-based HIPs are not well-matched with rapid development of computer vision techniques, since they are either vey simply passed or very hard to resolve, thus this motivate that continuous efforts are required to improve the development of HIPs base text. In this paper, a new proposed scheme is designed for animated text-based HIP; this scheme exploits the gap between the usual perception of human and the ability of computer to mimic this perception and to achieve more secured and more human usable HIP. This scheme could prevent attacks since it's hard for the machine to distinguish characters with animation environment displayed by digital video, but it's certainly still easy and practical to be used by humans because humans are attuned to perceiving motion easily. The proposed scheme has been tested by many Optical Character Recognition applications, and it overtakes all these tests successfully and it achieves a high usability rate of 95%.

Human Cognition in Automated Truing Test Design

Nowadays, many services in the internet including Email, search engine, social networking are provided with free of charge due to enormous growth of web users. With the expansion of Web services, denial of service (DoS) attacks by malicious automated programs (e.g., web bots) is becoming a serious problem of web service accounts. A HIP, or Human Interactive Proofs, is a human authentication mechanism that generates and grades tests to determine whether the user is a human or a malicious computer program. Unfortunately, the existing HIPs tried to maximize the difficulty for automated programs to pass tests by increasing distortion or noise. Consequently, it has also become difficult for potential users too. So there is a tradeoff between the usability and robustness in designing HIP tests. In their propose technique the authors tried to balance the readability and security by adding contextual information in the form of natural conversation without reducing the distortion and noise. In the result section, a microscopic large-scale user study was conducted involving 110 users to investigate the actual user views compare to existing state of the art CAPTCHA systems like Google's reCAPTCHA and Microsoft's CAPTCHA in terms of usability and security and found the authors' system capable of deploying largely over internet.

Script Familiarity and Its Effect on CAPTCHA Usability

Many web-based services such as email, search engines, and polling sites are being abused by spammers via computer programs known as bots. This problem has bred a new research area called Human Interactive Proofs (HIP) and a testing device called CAPTCHA, which aims to protect services from malevolent attacks by distinguishing bots from human users. In the past decade, researchers have focused on developing robust and safe HIP systems but have barely evaluated their usability. To begin to fill this gap, the authors report the results of a user study conducted to determine the extent that English language proficiency affects CAPTCHA usability for users whose native language is not English. The results showed a significant effect of participants’ English language proficiency level on the time the participant takes to solve CAPTCHA, which appear to be related to multiple usability issues including satisfaction and efficiency. Yet, they found that English language proficiency level does not affect the number of errors made while entering CAPTCHA or reCAPTCHA. The authors’ results have numerous implications that may inform future CAPTCHA design.