Machine learning for adversarial environments
Title: Machine learning for adversarial environments
DNr: SNIC 2022/5-531
Project Type: SNIC Medium Compute
Principal Investigator: György Dán <>
Affiliation: Kungliga Tekniska högskolan
Duration: 2022-12-01 – 2023-12-01
Classification: 10202


The project investigates robust machine learning algorithms in adversarial environments. Motivated by the well known vulnerability of deep neural networks to norm-bounded perturbation attacks and physically realizable attacks, the objective of the project is to develop algorithms and methods for detecting attacks combining Bayesian inference with sequential detection, and to use these algorithms and methods for developing effective methods and frameworks for incident response automation.