@article{5cd3bf8e9d97498ebfdd6c8bb79fc779,
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 “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.",
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",
volume = "3",
pages = "2160--2180",
journal = "Matter",
issn = "2590-2393",
publisher = "Cell Press",
number = "6",
}