Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication

Lin Jing; Qian Xie; Hongling Li; Kerui Li; Haitao Yang; Patricia Li Ping Ng; Shuo Li; Yang Li; Edwin Hang Tong Teo; Xiaonan Wang; Po Yen Chen

doi:10.1016/j.matt.2020.10.005

Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication

Lin Jing
, Qian Xie
, Hongling Li
, Kerui Li
, Haitao Yang
, Patricia Li Ping Ng
, Shuo Li
, Yang Li
, Edwin Hang Tong Teo
, Xiaonan Wang
, Po Yen Chen

National University of Singapore
Anhui University of Technology
Nanyang Technological University
University of Maryland, College Park

科研成果: 期刊稿件 › 文章 › 同行评审

40 引用（Scopus）

摘要

Multigenerational 2D-material (2DM) microstructures are fabricated via sequential deformations in a transfer-free fashion and exhibit physical unclonable function patterns with algorithm-recognizable features. Deep learning (DL)-facilitated software is developed on the basis of the “classification and validation” mechanism to shorten the authentication time. With 2DM tags and DL software, a reliable and environmentally stable anticounterfeiting technology, DeepKey, is realized to show superior encoding capacity and fast authentication, which can be applied as an add-on covert layer for QR codes to provide two-layer information security.

源语言	英语
页（从-至）	2160-2180
页数	21
期刊	Matter
卷	3
期	6
DOI	https://doi.org/10.1016/j.matt.2020.10.005
出版状态	已出版 - 2 12月 2020
已对外发布	是

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10.1016/j.matt.2020.10.005

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title = "Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication",

abstract = "Multigenerational 2D-material (2DM) microstructures are fabricated via sequential deformations in a transfer-free fashion and exhibit physical unclonable function patterns with algorithm-recognizable features. Deep learning (DL)-facilitated software is developed on the basis of the {\textquotedblleft}classification and validation{\textquotedblright} mechanism to shorten the authentication time. With 2DM tags and DL software, a reliable and environmentally stable anticounterfeiting technology, DeepKey, is realized to show superior encoding capacity and fast authentication, which can be applied as an add-on covert layer for QR codes to provide two-layer information security.",

keywords = "MAP4: Demonstrate, anticounterfeiting, deep learning, graphene oxide, hierarchical microstructures, physical unclonable functions, titanium carbide TiCT MXene",

author = "Lin Jing and Qian Xie and Hongling Li and Kerui Li and Haitao Yang and Ng, \{Patricia Li Ping\} and Shuo Li and Yang Li and Teo, \{Edwin Hang Tong\} and Xiaonan Wang and Chen, \{Po Yen\}",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors",

year = "2020",

month = dec,

day = "2",

doi = "10.1016/j.matt.2020.10.005",

language = "英语",

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TY - JOUR

T1 - Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication

AU - Jing, Lin

AU - Xie, Qian

AU - Li, Hongling

AU - Li, Kerui

AU - Yang, Haitao

AU - Ng, Patricia Li Ping

AU - Li, Shuo

AU - Li, Yang

AU - Teo, Edwin Hang Tong

AU - Wang, Xiaonan

AU - Chen, Po Yen

PY - 2020/12/2

Y1 - 2020/12/2

N2 - Multigenerational 2D-material (2DM) microstructures are fabricated via sequential deformations in a transfer-free fashion and exhibit physical unclonable function patterns with algorithm-recognizable features. Deep learning (DL)-facilitated software is developed on the basis of the “classification and validation” mechanism to shorten the authentication time. With 2DM tags and DL software, a reliable and environmentally stable anticounterfeiting technology, DeepKey, is realized to show superior encoding capacity and fast authentication, which can be applied as an add-on covert layer for QR codes to provide two-layer information security.

AB - Multigenerational 2D-material (2DM) microstructures are fabricated via sequential deformations in a transfer-free fashion and exhibit physical unclonable function patterns with algorithm-recognizable features. Deep learning (DL)-facilitated software is developed on the basis of the “classification and validation” mechanism to shorten the authentication time. With 2DM tags and DL software, a reliable and environmentally stable anticounterfeiting technology, DeepKey, is realized to show superior encoding capacity and fast authentication, which can be applied as an add-on covert layer for QR codes to provide two-layer information security.

KW - MAP4: Demonstrate

KW - anticounterfeiting

KW - deep learning

KW - graphene oxide

KW - hierarchical microstructures

KW - physical unclonable functions

KW - titanium carbide TiCT MXene

UR - https://www.scopus.com/pages/publications/85097052725

U2 - 10.1016/j.matt.2020.10.005

DO - 10.1016/j.matt.2020.10.005

M3 - 文章

AN - SCOPUS:85097052725

SN - 2590-2393

VL - 3

SP - 2160

EP - 2180

JO - Matter

JF - Matter

IS - 6

ER -

Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication

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