Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes
Document signature is a powerful technique used to determine whether a message is tampered or valid. Recently, this concept was extended to optical codes: we demonstrated that the combined use of optical techniques and machine learning algorithms might be able to distinguish among different classes...
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| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/9069171/ |
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| author | Kavan Ahmadi Pedro Latorre-Carmona Bahram Javidi Artur Carnicer |
| author_facet | Kavan Ahmadi Pedro Latorre-Carmona Bahram Javidi Artur Carnicer |
| author_sort | Kavan Ahmadi |
| collection | DOAJ |
| description | Document signature is a powerful technique used to determine whether a message is tampered or valid. Recently, this concept was extended to optical codes: we demonstrated that the combined use of optical techniques and machine learning algorithms might be able to distinguish among different classes of samples. In the present work, we produce nano particle encoded optical codes with predetermined designs synthesized with a 3D printer. We used conventional polylactic acid filament filled with metallic powder to include the effect of nano-encoding for unique polarimetric signatures. We investigated an interesting real-world scenario, that is, we demonstrate how a single class of codes is distinguished among a group of samples to be rejected. This is a difficult unbalanced problem since the number of polarimetric signatures that characterize the true class is small compared to the complete dataset. Each sample is characterized by analyzing the polarization state of the emerging light. Using the one class-support vector machine algorithm we found high accuracy figures in the recognition of the true class codes. To the best of our knowledge, this is the first report on implementing optical codes with nano particle encoded materials using 3D printing technology. |
| format | Article |
| id | doaj-art-2e9e64eda5c64e52a36ec11cb6fea81b |
| institution | DOAJ |
| issn | 1943-0655 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-2e9e64eda5c64e52a36ec11cb6fea81b2025-08-20T03:16:05ZengIEEEIEEE Photonics Journal1943-06552020-01-0112311010.1109/JPHOT.2020.29874849069171Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical CodesKavan Ahmadi0https://orcid.org/0000-0001-9647-4689Pedro Latorre-Carmona1https://orcid.org/0000-0001-6984-5173Bahram Javidi2https://orcid.org/0000-0002-3612-2873Artur Carnicer3https://orcid.org/0000-0002-4936-5778Departament de Física Aplicada, Universitat de Barcelona (UB), Barcelona, SpainDepartamento de Ingeniería Informática, Universidad de Burgos, Burgos, SpainDepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USADepartament de Física Aplicada, Universitat de Barcelona (UB), Barcelona, SpainDocument signature is a powerful technique used to determine whether a message is tampered or valid. Recently, this concept was extended to optical codes: we demonstrated that the combined use of optical techniques and machine learning algorithms might be able to distinguish among different classes of samples. In the present work, we produce nano particle encoded optical codes with predetermined designs synthesized with a 3D printer. We used conventional polylactic acid filament filled with metallic powder to include the effect of nano-encoding for unique polarimetric signatures. We investigated an interesting real-world scenario, that is, we demonstrate how a single class of codes is distinguished among a group of samples to be rejected. This is a difficult unbalanced problem since the number of polarimetric signatures that characterize the true class is small compared to the complete dataset. Each sample is characterized by analyzing the polarization state of the emerging light. Using the one class-support vector machine algorithm we found high accuracy figures in the recognition of the true class codes. To the best of our knowledge, this is the first report on implementing optical codes with nano particle encoded materials using 3D printing technology.https://ieeexplore.ieee.org/document/9069171/Optical authentication and securityoptical polarizationspeckle noise |
| spellingShingle | Kavan Ahmadi Pedro Latorre-Carmona Bahram Javidi Artur Carnicer Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes IEEE Photonics Journal Optical authentication and security optical polarization speckle noise |
| title | Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes |
| title_full | Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes |
| title_fullStr | Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes |
| title_full_unstemmed | Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes |
| title_short | Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes |
| title_sort | polarimetric identification of 3d printed nano particle encoded optical codes |
| topic | Optical authentication and security optical polarization speckle noise |
| url | https://ieeexplore.ieee.org/document/9069171/ |
| work_keys_str_mv | AT kavanahmadi polarimetricidentificationof3dprintednanoparticleencodedopticalcodes AT pedrolatorrecarmona polarimetricidentificationof3dprintednanoparticleencodedopticalcodes AT bahramjavidi polarimetricidentificationof3dprintednanoparticleencodedopticalcodes AT arturcarnicer polarimetricidentificationof3dprintednanoparticleencodedopticalcodes |