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 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.