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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9965441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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