Structured learning is the problem of finding a predictive model for mapping the input data into complex outputs that have some internal structure. Structured output prediction is a challenging task by itself, but the problem becomes even more difficult when the input data is adversarially manipulated to deceive the predictive model. The problem of adversarial structured output prediction is relatively new in the field of machine learning. Many real world applications can be abstracted as an adversarial structured output prediction problem. In this oral exam, I study the state-of-the-art methods for solving the problem of structured learning and output prediction in adversarial settings. In particular, I will mention the strengths and weaknesses of the existing methods, and point to the open problems in the field.