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New Cognitive Deep-Learning CAPTCHA

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots a...

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Autores principales: Trong, Nghia Dinh, Huong, Thien Ho, Hoang, Vinh Truong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965441/
https://www.ncbi.nlm.nih.gov/pubmed/36850935
http://dx.doi.org/10.3390/s23042338
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author Trong, Nghia Dinh
Huong, Thien Ho
Hoang, Vinh Truong
author_facet Trong, Nghia Dinh
Huong, Thien Ho
Hoang, Vinh Truong
author_sort Trong, Nghia Dinh
collection PubMed
description CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.
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spelling pubmed-99654412023-02-26 New Cognitive Deep-Learning CAPTCHA Trong, Nghia Dinh Huong, Thien Ho Hoang, Vinh Truong Sensors (Basel) Article CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA. MDPI 2023-02-20 /pmc/articles/PMC9965441/ /pubmed/36850935 http://dx.doi.org/10.3390/s23042338 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Trong, Nghia Dinh
Huong, Thien Ho
Hoang, Vinh Truong
New Cognitive Deep-Learning CAPTCHA
title New Cognitive Deep-Learning CAPTCHA
title_full New Cognitive Deep-Learning CAPTCHA
title_fullStr New Cognitive Deep-Learning CAPTCHA
title_full_unstemmed New Cognitive Deep-Learning CAPTCHA
title_short New Cognitive Deep-Learning CAPTCHA
title_sort new cognitive deep-learning captcha
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965441/
https://www.ncbi.nlm.nih.gov/pubmed/36850935
http://dx.doi.org/10.3390/s23042338
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