Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163592
Title: Physical unclonable function anti-counterfeiting labels with deep learning authentication
Authors: Sebastian, James
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Sebastian, J. (2022). Physical unclonable function anti-counterfeiting labels with deep learning authentication. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163592
Project: A2399-212 
Abstract: 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.
URI: https://hdl.handle.net/10356/163592
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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