"Exploring Comparative Visual Approaches for Understanding Model Trade-offs in Adversarial Machine Learning"  is a project that explores effective comparative visual techniques for model trade-off analysis in adversarial machine learning (AML). This work focuses on the need for practitioners to concurrently evaluate the accuracy and robustness of AI models across both standard and adversarial conditions. We identify and present effective visualization techniques for understanding AML trade-offs at different levels of detail. Then, based on insights gathered from an iterative design process, we designed VATRA, a design probe that supports multi-level comparative visualizations and enables machine learning practitioners to analyze trade-offs in adversarial training (AT) methods, which often come at the cost of reduced model accuracy and fairness.