| Title | Adaptive Fog-Based Output Security for Augmented Reality |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Ahn, Surin, Gorlatova, Maria, Naghizadeh, Parinaz, Chiang, Mung, Mittal, Prateek |
| Conference Name | Proceedings of the 2018 Morning Workshop on Virtual Reality and Augmented Reality Network |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5913-9 |
| Keywords | augmented reality, edge computing, Fog Computing, Fog Computing and Security, policy optimization, pubcrawl, reinforcement learning, Resiliency, Scalability, visual output security |
| Abstract | 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. |
| URL | http://doi.acm.org/10.1145/3229625.3229626 |
| DOI | 10.1145/3229625.3229626 |
| Citation Key | ahn_adaptive_2018 |
Adaptive Fog-Based Output Security for Augmented Reality