Deep learning enhanced anti-counterfeiting security tags made from thin films
In recent years, anti-counterfeiting methods have become increasingly important for ensuring the authenticity of physical objects. These methods can be categorized into physical, electronic, chemical, and mechanical methods. In this paper, we focus specifically on physical anti-counterfeiting method...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167576 |
| _version_ | 1826120161791311872 |
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| author | Low, Jing Yi |
| author2 | Y. C. Chen |
| author_facet | Y. C. Chen Low, Jing Yi |
| author_sort | Low, Jing Yi |
| collection | NTU |
| description | In recent years, anti-counterfeiting methods have become increasingly important for ensuring the authenticity of physical objects. These methods can be categorized into physical, electronic, chemical, and mechanical methods. In this paper, we focus specifically on physical anti-counterfeiting methods and investigate the feasibility of using machine learning to improve the accuracy and efficiency of identifying and authenticating Physical Unclonable Functions (PUFs). Our study aims to enhance existing solutions by exploring the potential of machine learning models in the context of PUFs. Through our experiments, we aim to provide a better understanding of the capabilities and limitations of this approach and to identify areas for future research. |
| first_indexed | 2024-10-01T05:11:43Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167576 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:11:43Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1675762023-07-07T15:53:32Z Deep learning enhanced anti-counterfeiting security tags made from thin films Low, Jing Yi Y. C. Chen School of Electrical and Electronic Engineering yucchen@ntu.edu.sg Engineering::Electrical and electronic engineering In recent years, anti-counterfeiting methods have become increasingly important for ensuring the authenticity of physical objects. These methods can be categorized into physical, electronic, chemical, and mechanical methods. In this paper, we focus specifically on physical anti-counterfeiting methods and investigate the feasibility of using machine learning to improve the accuracy and efficiency of identifying and authenticating Physical Unclonable Functions (PUFs). Our study aims to enhance existing solutions by exploring the potential of machine learning models in the context of PUFs. Through our experiments, we aim to provide a better understanding of the capabilities and limitations of this approach and to identify areas for future research. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-30T03:56:43Z 2023-05-30T03:56:43Z 2023 Final Year Project (FYP) Low, J. Y. (2023). Deep learning enhanced anti-counterfeiting security tags made from thin films. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167576 https://hdl.handle.net/10356/167576 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Low, Jing Yi Deep learning enhanced anti-counterfeiting security tags made from thin films |
| title | Deep learning enhanced anti-counterfeiting security tags made from thin films |
| title_full | Deep learning enhanced anti-counterfeiting security tags made from thin films |
| title_fullStr | Deep learning enhanced anti-counterfeiting security tags made from thin films |
| title_full_unstemmed | Deep learning enhanced anti-counterfeiting security tags made from thin films |
| title_short | Deep learning enhanced anti-counterfeiting security tags made from thin films |
| title_sort | deep learning enhanced anti counterfeiting security tags made from thin films |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167576 |
| work_keys_str_mv | AT lowjingyi deeplearningenhancedanticounterfeitingsecuritytagsmadefromthinfilms |