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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/167576 |
| الوسوم: |
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| الملخص: | 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. |
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