Physical unclonable function anti-counterfeiting labels with deep learning authentication
Physical Unclonable Function (PUF) is a recently developed anti-counterfeiting technique. the ability to generate strong anti-counterfeiting tags is the main reason for its vast development. Mostly, Convolutional Neural Network is used to authenticate these anti-counterfeiting tags due to its abi...
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格式: | Final Year Project |
語言: | English |
出版: |
Nanyang Technological University
2022
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在線閱讀: | https://hdl.handle.net/10356/163592 |
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機構: | Nanyang Technological University |
語言: | English |
總結: | Physical Unclonable Function (PUF) is a recently developed anti-counterfeiting technique.
the ability to generate strong anti-counterfeiting tags is the main reason for its vast development.
Mostly, Convolutional Neural Network is used to authenticate these anti-counterfeiting tags due to
its ability to automatically extract input image features. However, a very deep convolutional neural
network must deal with overfitting. In the PUF authentication process, the main cause of
overfitting is the minor alteration of PUF tags by a flow of time.
In this project, the Resnet-feature extraction pair model is proposed to deal with the overfitting
problem. The Resnet-feature extraction pair model combined extracted features from a convolution
neural network and extracted features from the mathematical computation. Subsequently, these
features are used to fit the Support Vector Machine. To evaluate its compatibility, the Resnet-feature
extraction pair model is implemented in PUF authentication process by using 8 true PUF tags and
356 fake PUF tags.
As a result, the Resnet-feature extraction pair model achieved a 15% accuracy improvement.
Hence, it can be concluded that the Resnet-feature extraction pair model is a considerable tool for
PUF authentication. |
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