  Many state-of-the-art ML models have outperformed humans in various tasks
such as image classification. With such outstanding performance, ML models are
widely used today. However, the existence of adversarial attacks and data
poisoning attacks really questions the robustness of ML models. For instance,
Engstrom et al. demonstrated that state-of-the-art image classifiers could be
easily fooled by a small rotation on an arbitrary image. As ML systems are
being increasingly integrated into safety and security-sensitive applications,
adversarial attacks and data poisoning attacks pose a considerable threat. This
chapter focuses on the two broad and important areas of ML security:
adversarial attacks and data poisoning attacks.
