Boosting adversarial training in safety-critical systems through boundary data selection

Boosting adversarial training in safety-critical systems through boundary data selection

AI-enabled collaborative robots are designed to be used in close collaboration with humans, thus requiring stringent safety standards and quick response times. Adversarial attacks pose a significant threat to the deep learning models of these systems, making it crucial to develop methods to improve...

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Bibliographic Details
Main Authors: JIA, Yifan, POSKITT, Christopher M., ZHANG, Peixin, WANG, Jingyi, SUN, Jun, CHATTOPADHYAY, Sudipta
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
Data selection
AI-enabled industrial systems
trustworthy systems
adversarial training
Databases and Information Systems
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/8303
https://ink.library.smu.edu.sg/context/sis_research/article/9306/viewcontent/rast_ral24.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/8303
https://ink.library.smu.edu.sg/context/sis_research/article/9306/viewcontent/rast_ral24.pdf

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