Work-in-progress: testing autonomous cyber-physical systems using fuzzing features from convolutional neural networks

Autonomous cyber-physical systems rely on modern machine learning methods such as deep neural networks to control their interactions with the physical world. Testing of such intelligent cyberphysical systems is a challenge due to the huge state space associated with high-resolution visual sensory in...

Full description

Saved in:
Bibliographic Details
Published in2017 International Conference on Embedded Software (EMSOFT) pp. 1 - 2
Main Authors Raj, Sunny, Jha, Sumit Kumar, Ramanathan, Arvind, Pullum, Laura L.
Format Conference Proceeding
LanguageEnglish
Published ACM 01.10.2017
Subjects
Autonomous automobiles
Cyber-physical systems
Filtering algorithms
Neural networks
Vehicles
Online AccessGet full text

Cover

More Information
Summary:Autonomous cyber-physical systems rely on modern machine learning methods such as deep neural networks to control their interactions with the physical world. Testing of such intelligent cyberphysical systems is a challenge due to the huge state space associated with high-resolution visual sensory inputs. We demonstrate how fuzzing the input using patterns obtained from the convolutional filters of an unrelated convolutional neural network can be used to test computer vision algorithms implemented in intelligent cyber-physical systems. Our method discovers interesting counterexamples to a pedestrian detection algorithm implemented in the popular OpenCV library. Our approach also unearths counterexamples to the correct behavior of an autonomous car similar to NVIDIA's end-to-end self-driving deep neural net running on the Udacity open-source simulator.
DOI:10.1145/3125503.3125568