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Article overview
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Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy | | Naman Patel
; Apoorva Nandini Saridena
; Anna Choromanska
; Prashanth Krishnamurthy
; Farshad Khorrami
; | | Date: |
12 Nov 2018 | | Abstract: | The paper proposes an on-line monitoring framework for continuous real-time
safety/security in learning-based control systems (specifically application to
a unmanned ground vehicle). We monitor validity of mappings from sensor inputs
to actuator commands, controller-focused anomaly detection (CFAM), and from
actuator commands to sensor inputs, system-focused anomaly detection (SFAM).
CFAM is an image conditioned energy based generative adversarial network
(EBGAN) in which the energy based discriminator distinguishes between proper
and anomalous actuator commands. SFAM is based on an action condition video
prediction framework to detect anomalies between predicted and observed
temporal evolution of sensor data. We demonstrate the effectiveness of the
approach on our autonomous ground vehicle for indoor environments and on
Udacity dataset for outdoor environments. | | Source: | arXiv, 1811.4539 | | Services: | Forum | Review | PDF | Favorites | |
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