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A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the...

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Detalles Bibliográficos
Autores principales: Wang, Tianyu, Zheng, Hong, You, Changhui, Ju, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863413/
https://www.ncbi.nlm.nih.gov/pubmed/36679589
http://dx.doi.org/10.3390/s23020795
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author Wang, Tianyu
Zheng, Hong
You, Changhui
Ju, Jianping
author_facet Wang, Tianyu
Zheng, Hong
You, Changhui
Ju, Jianping
author_sort Wang, Tianyu
collection PubMed
description This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.
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spelling pubmed-98634132023-01-22 A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method Wang, Tianyu Zheng, Hong You, Changhui Ju, Jianping Sensors (Basel) Article This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance. MDPI 2023-01-10 /pmc/articles/PMC9863413/ /pubmed/36679589 http://dx.doi.org/10.3390/s23020795 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
Wang, Tianyu
Zheng, Hong
You, Changhui
Ju, Jianping
A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method
title A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method
title_full A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method
title_fullStr A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method
title_full_unstemmed A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method
title_short A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method
title_sort texture-hidden anti-counterfeiting qr code and authentication method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863413/
https://www.ncbi.nlm.nih.gov/pubmed/36679589
http://dx.doi.org/10.3390/s23020795
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