New Directions in Adversarial Machine Learning: From Theory to Applications

Project Description

Systems supported by machine learning (ML) algorithms have brought significant benefit to our daily life. With the growing deployment of such systems, the security of them has become a major concern in many application domains. This project aims to address the security concern in three main directions:

i) analysing the adversarial ML from the game theoretic view,

ii) expanding the adversarial ML to take into account more complex learning paradigms and

iii) considering adversarial ML on graph-structured data.

This project benefits the ML research by providing frameworks for identifying the vulnerability of ML algorithms and developing defense strategies to make ML more secure. Moreover, this project builds connection between game theory and ML research by modelling the attackers and learners as game-players, which enriches the game theoretic frameworks. In addition, this project will develop novel optimization techniques to compute attack and defense strategies, which also enriches the optimization research.

Research Technical Area

  • Game theory and economic paradigms
  • Machine learning
  • Adversarial examples

Benefits to the society

The deliverable of our project can be potentially used to improve security of many domains, including commercial recommender systems, self-driving vehicles, financial models and smart traffic control systems.

Project’s Publications

Team’s Principal Investigator

Associate Professor Bo An

Nanyang Technological University

Prof Bo An is a President’s Council Chair Associate Professor at NTU. He received a Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His research interests include artificial intelligence, multi-agent systems, computational game theory, reinforcement learning, and optimisation.

Recent Notable Awards

  • AAAI Senior Member, 2019

  • AI’s 10 to Watch, 2018

  • Winner of the Microsoft Collaborative AI Challenge, 2017

The Team


Dr. Milind Tambe, University of Southern California
Research Focus: AI, multi-agent systems, computational and behavioral game theory

Associate Professor Yevgeniy Vorobeychik, Washington University
Research Focus: Research game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory and incentive design