Abstract In security-related applications, an adversary is able to fool a model by using carefully crafted samples. A traditional machine learning method may be compromised through an adversarial attack that violates the implicit assumption of the same distributions on training and test samples. This security problem may become more serious in deep learning since public dataset and pre-trained models are used more frequently in recent years, and those datasets and models can be easily compromised by a nefarious third party supplier. This tutorial will introduce the concept of how vulnerable a machine learning method is and how its robustness can be improved, and also discuss real life machine learning applications in an adversarial environment. Speaker(s): Associate Professor Patrick Chan Room: G04, Bldg: Napier Building, The University of Adelaide, North Terrace Campus, ADELAIDE, South Australia, Australia, 5005