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

• 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
• Institution: Singapore Management University

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...