Puzzle Based Captcha Implementation for Noisy Environment
Abstract
Today, it is a very common problem that bots attack on the online polls and register free email accounts automatically that increase the congestion on network as well as consume large amount of server space. Therefore, to prevent these kinds of attacks, a technique called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) have been used. The main motive of present research work is to design a CAPTCHA in order to increase the security by preventing bot attacks using random mathematical functions and background noise makes it invulnerable for the optical recognition (OCR) technique to break the CAPTCHA as OCR attack is capable of only extracting characters from an image. To make CAPTCHA more secure, cross operations has been embedded in the present algorithm which makes it impossible for OCR technique to decode its output result.
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Introduction
CAPTCHA is an acronym for Completely Automated Public Turing test to tell Computers and Humans Apart. The term "CAPTCHA" was coined in 2000 by Luis Von Ahn [1], Manuel Blum, Nicholas J. Hopper (all of Carnegie Mellon University, and John Langford (then of IBM). They are challenge-response tests to ensure that the users are human not a bot. The use of a CAPTCHA is to block submissions of spam bots that can be harvest email addresses from publicly. A very common kind of CAPTCHA which is used on websites requires the users to enter the string of distorted string of characters on the screen. CAPTCHAs are used because it is difficult for the computers to extract or understand the distorted text or image, whereas it is relatively easy for a human to understand that text or image. Therefore, the correct response to a CAPTCHA challenge is assumed to come from a human and the user is permitted into the website.
The need for CAPTCHAs rose to keep out the website or search engine from bots. In 1997, AltaVista developed a method to generate a printed text randomly that only humans could read and not machine readersThe existing CAPTCHAs can be generally classified into three categories: Image-based CAPTCHAs [2, 3], Text-based CAPTCHAs [4, 5] and Sound-based CAPTCHAs [6]. Text-based CAPTCHAs that depend on the distortion of digits,letters and other visual effects added in the background image. The user is asked to identify the distorted characters and entered them. So far, most commonly used CAPTCHAs are text-based CAPTCHAs. They can be easily designed and implemented and can be easily solved by users.. Examples of text and Graphic CAPTCHAs include:
EZGimpy: Pick a word or words from a small dictionary. Distort them, add noise and background.
Gimpy-r: Pick random letters. Distort them, add noise and background.
Baffle Text: Pick random Alphabets which create nonsense but pronounceable words.
Bongo: User has to solve pattern reorganization problem by finding which figure belongs to which one.
Pix: User has to recognize the common features from a set of images.
The organization of the paper trails as: Review of previous related work is given in Section. II. Section III focuses on the formulation of the proposed algorithm. Section IV reports a number of experimental results to demonstrate the performance of the new algorithm. Finally, conclusions are drawn in Section. V.
Conclusion
In this research a new captcha design approach has been implemented using simple mathematical equations and puzzles. The result shows that the proposed approach is more secure and simple as compare to others. Furthermore, after applying OCR attacks user can conclude that our proposed method is more secure to bot attacks and is very useful for high level of security programs.
This research work can be further extended or enhance by including other techniques along with math captcha with Boolean expressions, number system to make it more rebust and unbreakable.