Property-Driven Quality Assurance of Adversarial Robustness of Deep Neural Networks

KAKEN

About This Project

Japan Grant Number
JP23K11049 (JGN)
Funding Program
Grants-in-Aid for Scientific Research
Funding Organization
Japan Society for the Promotion of Science

Kakenhi Information

Project/Area Number
23K11049
Research Category
Grant-in-Aid for Scientific Research (C)
Allocation Type
  • Multi-year Fund
Review Section / Research Field
  • Basic Section 60050:Software-related
Research Institution
  • The University of Tokyo
Project Period (FY)
2023-04-01 〜 2026-03-31
Project Status
Granted
Budget Amount*help
4,680,000 Yen (Direct Cost: 3,600,000 Yen Indirect Cost: 1,080,000 Yen)

Research Abstract

Deep neural networks (DNNs) have achieved tremendous success in various areas. However, they are vulnerable to adversarial examples, which has posed severe security risks for DNNs. We propose to develop practical and scalable techniques for quality assurance of adversarial robustness of DNNs.

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