Volume- 12
Issue- 2
Year- 2024
DOI: 10.55524/ijircst.2024.12.2.3 | DOI URL: https://doi.org/10.55524/ijircst.2024.12.2.3 Crossref
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Dayanand , Wilson Jeberson, Klinsega Jeberson
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems serve as a crucial defense mechanism against automated attacks by distinguishing between human users and bots. However, advancements in deep learning have posed significant challenges to the security of conventional CAPTCHA systems. In this research paper, we present a comparative analysis of two prominent object detection algorithms, Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN), for breaking CAPTCHAs. The study evaluates the performance of CNN and RCNN algorithms in accurately identifying and deciphering characters within CAPTCHA images. Utilizing a diverse dataset of CAPTCHA samples, experiments are conducted to assess the effectiveness of both algorithms in handling variations in CAPTCHA styles, languages, and complexities. Through extensive experimentation and evaluation, we analyze the strengths and limitations of CNN and RCNN in the context of CAPTCHA breaking. Key metrics such as accuracy, precision, recall, and computational efficiency are compared to provide insights into the relative performance of each algorithm. The findings of this research contribute to the understanding of object detection techniques for CAPTCHA breaking and provide valuable insights for enhancing the security of CAPTCHA systems against emerging threats posed by deep learning-based attacks.
Research Scholar, Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj, India
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Anshjyot Singh Wadhwa.
September 2024 - Vol 12, Issue 5
Mohammad Hashir, Sanskar Mishra, Yojna Arora, Avinash Kumar Sharma.
September 2024 - Vol 12, Issue 5
Mohit Apte.
July 2024 - Vol 12, Issue 4