Deep Learning-Enhanced Dynamic Photonic Security System Using Multimode VCSELs

  • Nakul Nandhakumar
  • , Zhican Zhou
  • , Hang Lu
  • , Omar Alkhazragi
  • , Tien Khee Ng
  • , Boon S. Ooi
  • , Yating Wan*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a photonic security system leveraging dynamic physical unclonable functions generated by multimode VCSELs, combined with deep learning-enhanced authentication. This system enables ultra-fast key distribution (< 10ns) and ∼ 100% specificity, ideal for robust anti-counterfeiting.

Original languageEnglish
Title of host publication2025 Conference on Lasers and Electro-Optics, CLEO 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781957171500
StatePublished - 2025
Externally publishedYes
Event2025 Conference on Lasers and Electro-Optics, CLEO 2025 - Long Beach, United States
Duration: 4 May 20259 May 2025

Publication series

Name2025 Conference on Lasers and Electro-Optics, CLEO 2025

Conference

Conference2025 Conference on Lasers and Electro-Optics, CLEO 2025
Country/TerritoryUnited States
CityLong Beach
Period4/05/259/05/25

Bibliographical note

Publisher Copyright:
© 2025 Optica.

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Computer Networks and Communications
  • Civil and Structural Engineering
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Instrumentation

Fingerprint

Dive into the research topics of 'Deep Learning-Enhanced Dynamic Photonic Security System Using Multimode VCSELs'. Together they form a unique fingerprint.

Cite this