Approach to Security Provision of Machine Vision for Unmanned Vehicles of “Smart City”
Title | Approach to Security Provision of Machine Vision for Unmanned Vehicles of “Smart City” |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Iskhakov, A., Jharko, E. |
Conference Name | 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) |
Date Published | May 2020 |
Publisher | IEEE |
ISBN Number | 978-1-7281-4590-7 |
Keywords | contour 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. |
URL | https://ieeexplore.ieee.org/document/9112047 |
DOI | 10.1109/ICIEAM48468.2020.9112047 |
Citation Key | iskhakov_approach_2020 |
- pubcrawl
- unmanned vehicles
- unmanned vehicle hardware platforms
- Smart City
- smart cities
- security provision
- security
- robotic complexes
- robot vision
- robot operating systems
- Resiliency
- resilience
- remotely operated vehicles
- contour analysis
- policy-based governance
- objects detection
- object detection
- Neural networks
- neural nets
- machine vision systems
- learning (artificial intelligence)
- information security systems
- Human Factors
- Human behavior
- feature squeezing method
- deep learning