Adaptive AR visual output security using reinforcement learning trained policies: Demo abstract


Conference Paper

© 2019 Authors. Augmented reality (AR) technologies have seen significant improvement in recent years with several consumer and commercial solutions being developed. New security challenges arise as AR becomes increasingly ubiquitous. Previous work has proposed techniques for securing the output of AR devices and used reinforcement learning (RL) to train security policies which can be difficult to define manually. However, whether such systems and policies can be deployed on a physical AR device without degrading performance was left an open question. We develop a visual output security application using a RL trained policy and deploy it on a Magic Leap One head-mounted AR device. The demonstration illustrates that RL based visual output security systems are feasible.

Full Text

Duke Authors

Cited Authors

  • Dechicchis, J; Ahn, S; Gorlatova, M

Published Date

  • November 10, 2019

Published In

  • Sensys 2019 Proceedings of the 17th Conference on Embedded Networked Sensor Systems

Start / End Page

  • 380 - 381

International Standard Book Number 13 (ISBN-13)

  • 9781450369503

Digital Object Identifier (DOI)

  • 10.1145/3356250.3361935

Citation Source

  • Scopus