ARC Future Fellowships - Grant ID: FT220100318

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/FT220100318

Funding Status
Active

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Funded Activity Summary

Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the next generation of intelligent systems to accommodate data in a noisy and hostile environment. This should benefit science, society, and the economy nationally and internationally through the applications to trustworthily analyse their corresponding complex data.

Funded Activity Details

Start Date: 30-06-2023

End Date: 29-06-2027

Funding Scheme: ARC Future Fellowships

Funding Amount: $764,534.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Adversarial machine learning | Machine learning | Semi- and unsupervised learning |

ANZSRC Socio-Economic Objective (SEO)

Other Keywords

Adversarial machine learning | Artificial Intelligence | Machine learning | Semi- and unsupervised learning

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