4.8 Article

Triple-layer unclonable anti-counterfeiting enabled by huge-encoding capacity algorithm and artificial intelligence authentication

期刊

NANO TODAY
卷 41, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.nantod.2021.101324

关键词

Silicon nanohybrids; Triple-layer anti-counterfeiting; Artificial Intelligence; Physical; Unclonable functions

资金

  1. National Natural Science Foundation of China [21825402, 22074101]
  2. Natural Science Foundation of Jiangsu Province of China [BK20191417, BK20200851]
  3. China Postdoctoral Science Foundation [2021M692347]
  4. Program for Jiangsu Specially-Appointed Professors - Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
  5. Suzhou Key Laboratory of Nanotechnology and Biomedicine and the Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC)

向作者/读者索取更多资源

The security solution utilizes multi-functional nanoinks to create fluorescent and plasmonic security tags, with a triple-layer authentication model combining physical unclonable functions (PUFs), huge-encoding capacity algorithm, and artificial intelligence techniques.
As a fundamental security problem, counterfeits pose a tremendous threat to public health and social economy. Herein, we exploit multi-functional nanoinks made of one-dimensional silicon-based nanohy-brids for constructing fluorescent and plasmonic security tags. Of particular significance, the presented security solution exhibits triple-layer authentication model, simultaneously featuring the advantages of physical unclonable functions (PUFs), huge-encoding capacity algorithm and artificial intelligence technique. In macroscale, the multi-color fluorescence security signals are used as the first layer, which can be verified through portable smartphone. In the second security layer, the unclonable surface-enhanced Raman scattering (SERS) security signals at low-level magnification could be visualized using confocal Raman system. Taking advantages of coarse grained and quaternary encrypting of signals from Raman at each pixel, the encoding capacity reaches 6.43 x 10(24082), which is much higher than the value (i.e., 3 x 10(15051)) ever reported. In the third layer, the aggregated SERS signals at high-level magnification Raman mapping produce unrepeatable patterns with shape-specific information. By further applying specifically artificial intelligence (AI), faint features of different SERS images are extracted and trained, allowing 98-100% of recognition accuracy after 1000 learning cycles. Such triple-layer security solution ensures the PUF5, huge encoding capacity and AI authentication simultaneously, providing newly high-performance platform of unbreakable anti-counterfeiting. (C) 2021 Elsevier Ltd. All rights reserved.

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