Intrusion detection on robot cameras using spatio-temporal autoencoders: A self-driving car application

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing...

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Bibliographic Details
Published in:IEEE Vehicular Technology Conference pp. 1 - 5
Main Authors: Amrouche, Faouzi, Lagraa, Sofiane, Frank, Raphael, State, Radu
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2020
Subjects:
Autonomous automobiles
Cameras
Image reconstruction
Robot vision systems
Security
Training
ISSN:2577-2465
Online Access:Get full text
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Summary:Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.
ISSN:2577-2465
DOI:10.1109/VTC2020-Spring48590.2020.9129461