De-CAPTCHA: A novel DFS based approach to solve CAPTCHA schemes. (January 2022)

• Record Type: Journal Article
• Authors: Atri, Aditya; Bansal, Ankita; Khari, Manju; Vimal, S.
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: CAPTCHA -- Island Problem -- Depth-first Search -- Security -- Convolutional Neural Network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107593
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: CAPTCHA stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart. CAPTCHAs are used as security mechanism in web applications to differentiate between real users and automated users, also known as bots. Text-based CAPTCHAs are the popularly used CAPTCHA schemes due to their simplicity and thus, they are still being used despite the proposal of several attack mechanisms. In this work, the authors have proposed a novel approach to solve CAPTCHA schemes. In this approach, the authors have used Depth First Search algorithm for the extraction of characters from CAPTCHAs and Convolutional Neural Network for recognizing these extracted characters. The proposed approach was validated on 3000+ CAPTCHA schemes and proved to be efficient by providing an average accuracy of more than 92.0% in detecting CAPTCHA schemes. Graphical abstract: Highlights: Achieved an average Accuracy Rate of more than 92.0% in detecting CAPTCHA schemes. The model solved the CAPTCHA schemes in IRCTC with average Accuracy Rate of 98.0%. The schemes in UIDAI, Digital India are solved with average Accuracy Rate of 90.6%.