  This paper studies the reliability of a real-world learning-enabled system,
which conducts dynamic vehicle tracking based on a high-resolution wide-area
motion imagery input. The system consists of multiple neural network components
-- to process the imagery inputs -- and multiple symbolic (Kalman filter)
components -- to analyse the processed information for vehicle tracking. It is
known that neural networks suffer from adversarial examples, which make them
lack robustness. However, it is unclear if and how the adversarial examples
over learning components can affect the overall system-level reliability. By
integrating a coverage-guided neural network testing tool, DeepConcolic, with
the vehicle tracking system, we found that (1) the overall system can be
resilient to some adversarial examples thanks to the existence of other
components, and (2) the overall system presents an extra level of uncertainty
which cannot be determined by analysing the deep learning components only. This
research suggests the need for novel verification and validation methods for
learning-enabled systems.
