High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning
Abstract Physical unclonable function labels have emerged as a promising candidate for achieving unbreakable anticounterfeiting. Despite their significant progress, two challenges for developing practical physical unclonable function systems remain, namely 1) fairly few high-dimensional encoded labe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55014-2 |
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| _version_ | 1849220638914379776 |
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| author | Lingzhi Wang Xin Yu Tongtong Zhang Yong Hou Dangyuan Lei Xiaojuan Qi Zhiqin Chu |
| author_facet | Lingzhi Wang Xin Yu Tongtong Zhang Yong Hou Dangyuan Lei Xiaojuan Qi Zhiqin Chu |
| author_sort | Lingzhi Wang |
| collection | DOAJ |
| description | Abstract Physical unclonable function labels have emerged as a promising candidate for achieving unbreakable anticounterfeiting. Despite their significant progress, two challenges for developing practical physical unclonable function systems remain, namely 1) fairly few high-dimensional encoded labels with excellent material properties, and 2) existing authentication methods with poor noise tolerance or inapplicability to unseen labels. Herein, we employ the linear polarization modulation of randomly distributed fluorescent nanodiamonds to demonstrate, for the first time, three-dimensional encoding for diamond-based labels. Briefly, our three-dimensional encoding scheme provides digitized images with an encoding capacity of 109771 and high distinguishability under a short readout time of 7.5 s. The high photostability and inertness of fluorescent nanodiamonds endow our labels with high reproducibility and long-term stability. To address the second challenge, we employ a deep metric learning algorithm to develop an authentication methodology that computes the similarity of deep features of digitized images, exhibiting a better noise tolerance than the classical point-by-point comparison method. Meanwhile, it overcomes the key limitation of existing artificial intelligence-driven classification-based methods, i.e., inapplicability to unseen labels. Considering the high performance of both fluorescent nanodiamonds labels and deep metric learning authentication, our work provides the basis for developing practical physical unclonable function anticounterfeiting systems. |
| format | Article |
| id | doaj-art-3298eea4e98e491889841f3dece70769 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3298eea4e98e491889841f3dece707692024-12-08T12:37:01ZengNature PortfolioNature Communications2041-17232024-12-0115111310.1038/s41467-024-55014-2High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learningLingzhi Wang0Xin Yu1Tongtong Zhang2Yong Hou3Dangyuan Lei4Xiaojuan Qi5Zhiqin Chu6Department of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Materials Science and Engineering, City University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongAbstract Physical unclonable function labels have emerged as a promising candidate for achieving unbreakable anticounterfeiting. Despite their significant progress, two challenges for developing practical physical unclonable function systems remain, namely 1) fairly few high-dimensional encoded labels with excellent material properties, and 2) existing authentication methods with poor noise tolerance or inapplicability to unseen labels. Herein, we employ the linear polarization modulation of randomly distributed fluorescent nanodiamonds to demonstrate, for the first time, three-dimensional encoding for diamond-based labels. Briefly, our three-dimensional encoding scheme provides digitized images with an encoding capacity of 109771 and high distinguishability under a short readout time of 7.5 s. The high photostability and inertness of fluorescent nanodiamonds endow our labels with high reproducibility and long-term stability. To address the second challenge, we employ a deep metric learning algorithm to develop an authentication methodology that computes the similarity of deep features of digitized images, exhibiting a better noise tolerance than the classical point-by-point comparison method. Meanwhile, it overcomes the key limitation of existing artificial intelligence-driven classification-based methods, i.e., inapplicability to unseen labels. Considering the high performance of both fluorescent nanodiamonds labels and deep metric learning authentication, our work provides the basis for developing practical physical unclonable function anticounterfeiting systems.https://doi.org/10.1038/s41467-024-55014-2 |
| spellingShingle | Lingzhi Wang Xin Yu Tongtong Zhang Yong Hou Dangyuan Lei Xiaojuan Qi Zhiqin Chu High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning Nature Communications |
| title | High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning |
| title_full | High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning |
| title_fullStr | High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning |
| title_full_unstemmed | High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning |
| title_short | High-dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning |
| title_sort | high dimensional anticounterfeiting nanodiamonds authenticated with deep metric learning |
| url | https://doi.org/10.1038/s41467-024-55014-2 |
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