Adversarial Machine Learning and Defense Strategies

8 Dec 2022 | 3.00pm (Singapore Time)

Adversarial attacks can disrupt artificial intelligence (AI) and machine learning (ML) based system functionalities but also provide significant research opportunities. In this talk, Prof Dipankar Dasgupta from The University of Memphis will cover emerging adversarial machine learning (AML) attacks on systems and the state-of-the-art defense techniques. Prof Dasgupta will first discuss how and where adversarial attacks could happen in an AI/ML model and framework. He will then present the classification of adversarial attacks and their severity and applicability in real-world problems, including the steps to mitigate their effects, before illustrating the role of GAN in adversarial attacks and as a defence strategy.

Finally, Prof Dasgupta will also discuss a dual-filtering (DF) strategy that could mitigate adaptive or advanced adversarial manipulations for a wide-range of ML attacks with higher accuracy. The developed DF software could be used as a wrapper to any existing ML-based decision support system to prevent a wide variety of adversarial evasion attacks. The DF framework utilises two set of filters based on positive (input filters) and negative (output filters) verification strategies that could communicate with each other for higher robustness.