Wang Lingzhi, Yu Xin, Zhang Tongtong, Hou Yong, Lei Dangyuan, Qi Xiaojuan, Chu Zhiqin
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China.
Nat Commun. 2024 Dec 5;15(1):10602. doi: 10.1038/s41467-024-55014-2.
Physical unclonable function labels have emerged as a promising candidate for achieving unbreakable anticounterfeiting. Despite their significant progress, two challenges for developing practical physical unclonable function systems remain, namely 1) fairly few high-dimensional encoded labels with excellent material properties, and 2) existing authentication methods with poor noise tolerance or inapplicability to unseen labels. Herein, we employ the linear polarization modulation of randomly distributed fluorescent nanodiamonds to demonstrate, for the first time, three-dimensional encoding for diamond-based labels. Briefly, our three-dimensional encoding scheme provides digitized images with an encoding capacity of 10 and high distinguishability under a short readout time of 7.5 s. The high photostability and inertness of fluorescent nanodiamonds endow our labels with high reproducibility and long-term stability. To address the second challenge, we employ a deep metric learning algorithm to develop an authentication methodology that computes the similarity of deep features of digitized images, exhibiting a better noise tolerance than the classical point-by-point comparison method. Meanwhile, it overcomes the key limitation of existing artificial intelligence-driven classification-based methods, i.e., inapplicability to unseen labels. Considering the high performance of both fluorescent nanodiamonds labels and deep metric learning authentication, our work provides the basis for developing practical physical unclonable function anticounterfeiting systems.
物理不可克隆功能标签已成为实现牢不可破防伪技术的一个有前途的候选方案。尽管取得了重大进展，但开发实用的物理不可克隆功能系统仍面临两个挑战，即：1）具有优异材料特性的高维编码标签相当少；2）现有的认证方法噪声容忍度低或不适用于未见过的标签。在此，我们利用随机分布的荧光纳米金刚石的线性偏振调制，首次展示了基于金刚石的标签的三维编码。简而言之，我们的三维编码方案在7.5秒的短读出时间内提供了编码容量为10且具有高可区分性的数字化图像。荧光纳米金刚石的高光稳定性和惰性赋予了我们的标签高再现性和长期稳定性。为了解决第二个挑战，我们采用深度度量学习算法开发了一种认证方法，该方法计算数字化图像深度特征的相似度，与经典的逐点比较方法相比，具有更好的噪声容忍度。同时，它克服了现有人工智能驱动的基于分类方法的关键局限性，即不适用于未见过的标签。考虑到荧光纳米金刚石标签和深度度量学习认证的高性能，我们的工作为开发实用的物理不可克隆功能防伪系统提供了基础。
