Visible to the public Approach to Security Provision of Machine Vision for Unmanned Vehicles of “Smart City”

TitleApproach to Security Provision of Machine Vision for Unmanned Vehicles of “Smart City”
Publication TypeConference Paper
Year of Publication2020
AuthorsIskhakov, A., Jharko, E.
Conference Name2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
Date PublishedMay 2020
PublisherIEEE
ISBN Number978-1-7281-4590-7
Keywordscontour analysis, Deep Learning, feature squeezing method, Human Behavior, human factors, information security systems, learning (artificial intelligence), machine vision systems, neural nets, Neural networks, object detection, objects detection, policy-based governance, pubcrawl, remotely operated vehicles, resilience, Resiliency, robot operating systems, robot vision, robotic complexes, security, security provision, smart cities, smart city, unmanned vehicle hardware platforms, unmanned vehicles
Abstract

By analogy to nature, sight is the main integral component of robotic complexes, including unmanned vehicles. In this connection, one of the urgent tasks in the modern development of unmanned vehicles is the solution to the problem of providing security for new advanced systems, algorithms, methods, and principles of space navigation of robots. In the paper, we present an approach to the protection of machine vision systems based on technologies of deep learning. At the heart of the approach lies the "Feature Squeezing" method that works on the phase of model operation. It allows us to detect "adversarial" examples. Considering the urgency and importance of the target process, the features of unmanned vehicle hardware platforms and also the necessity of execution of tasks on detecting of the objects in real-time mode, it was offered to carry out an additional simple computational procedure of localization and classification of required objects in case of crossing a defined in advance threshold of "adversarial" object testing.

URLhttps://ieeexplore.ieee.org/document/9112047
DOI10.1109/ICIEAM48468.2020.9112047
Citation Keyiskhakov_approach_2020