Due to the significant increase in the size of the internet and the number of users on this platform there has been a tremendous increase in load on various websites and web-based applications. This load is from the user end which causes unforeseen conditions which leads to unacceptable consequences such as crash or a data loss scenario at the webserver end. Therefore, there is a need to reduce the load on the server as well as the chances of network attacks that increase with the increased user base. The undue consequences such as data loss and server crash are caused due to two main reasons: the first one being an overload of users and the second due to an increased number of automatic programs or robots. A technique can be utilized to overcome this scenario by introducing a delay in the operation speed on the user end through the use of a CAPTCHA mechanism. Most of the classical approaches use a single method for the generation of the CAPTCHA, to overcome this proposed model uses the versatile image CAPTCHA generation mechanism. We have introduced a system that utilizes manualbased, face detection-based, colour based and random object insertion technique to generate 4 different random types of CAPTCHA. The proposed methodology implements a region of interest and convolutional neural networks to achieve the generation of the CAPTCHA effectively.

Versatile CAPTCHA Generation Using Machine Learning and Image Processing

Authentication is a commonly used technique in software applications to ensure whether the correct user is getting access to secured services or not. Due to the fast pace of data growth, there is an increasing need for security of online data. This is commonly achieved by the use of various CAPTCHAs. CAPTCHAs are an implementation of reverse Turing test. Turing test is used to distinguish between humans and computers. A fundamental property that the use of CAPTCHA necessitates is that the CAPTCHA must be easy and efficient for humans but difficult for computers. CAPTCHAs are used to protect the data from malicious bots and web crawlers. They are not only used in web sites but also used in banking transactions, cloud computing, distributed infrastructures and income tax services. In this paper, an analysis of the different categories of CAPTCHAs, the attacks against them and ideas and designs to develop more robust and user-friendly CAPTCHAs in the coming future are presented.

Swarnima Deshmukh

Papers

Versatile CAPTCHA Generation Using Machine Learning and Image Processing

An Overview of Various Types of CAPTCHA