TY - GEN
T1 - Adaptive fog-based output security for augmented reality
AU - Ahn, Surin
AU - Gorlatova, Maria
AU - Naghizadeh, Parinaz
AU - Chiang, Mung
AU - Mittal, Prateek
N1 - Funding Information:
This work was supported in part by the Department of Electrical Engineering and the School of Engineering and Applied Science at Princeton University, the Comcast Innovation Fund Research Grant, and Defense Advanced Research Projects Agency (DARPA), under contract No. HR001117C0052 and No. HR001117C0048. The opinions, findings and conclusions expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency. We thank Dr. Andrew S. Lan for useful discussions, the participants of our user study for their time, and the reviewers for helpful feedback.
Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/8/7
Y1 - 2018/8/7
N2 - Augmented reality (AR) technologies are rapidly being adopted across multiple sectors, but little work has been done to ensure the security of such systems against potentially harmful or distracting visual output produced by malicious or bug-ridden applications. Past research has proposed to incorporate manually specified policies into AR devices to constrain their visual output. However, these policies can be cumbersome to specify and implement, and may not generalize well to complex and unpredictable environmental conditions. We propose a method for generating adaptive policies to secure visual output in AR systems using deep reinforcement learning. This approach utilizes a local fog computing node, which runs training simulations to automatically learn an appropriate policy for filtering potentially malicious or distracting content produced by an application. Through empirical evaluations, we show that these policies are able to intelligently displace AR content to reduce obstruction of real-world objects, while maintaining a favorable user experience.
AB - Augmented reality (AR) technologies are rapidly being adopted across multiple sectors, but little work has been done to ensure the security of such systems against potentially harmful or distracting visual output produced by malicious or bug-ridden applications. Past research has proposed to incorporate manually specified policies into AR devices to constrain their visual output. However, these policies can be cumbersome to specify and implement, and may not generalize well to complex and unpredictable environmental conditions. We propose a method for generating adaptive policies to secure visual output in AR systems using deep reinforcement learning. This approach utilizes a local fog computing node, which runs training simulations to automatically learn an appropriate policy for filtering potentially malicious or distracting content produced by an application. Through empirical evaluations, we show that these policies are able to intelligently displace AR content to reduce obstruction of real-world objects, while maintaining a favorable user experience.
KW - Augmented reality
KW - Edge computing
KW - Fog computing
KW - Policy optimization
KW - Reinforcement learning
KW - Visual output security
UR - http://www.scopus.com/inward/record.url?scp=85056428916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056428916&partnerID=8YFLogxK
U2 - 10.1145/3229625.3229626
DO - 10.1145/3229625.3229626
M3 - Conference contribution
AN - SCOPUS:85056428916
T3 - VR/AR Network 2018 - Proceedings of the 2018 Morning Workshop on Virtual Reality and Augmented Reality Network, Part of SIGCOMM 2018
SP - 1
EP - 6
BT - VR/AR Network 2018 - Proceedings of the 2018 Morning Workshop on Virtual Reality and Augmented Reality Network, Part of SIGCOMM 2018
PB - Association for Computing Machinery, Inc
T2 - 2nd ACM SIGCOMM Workshop on Virtual Reality and Augmented Reality Network, VR/AR Network 2018
Y2 - 24 August 2018
ER -