  In the realm of visual recognition, data augmentation stands out as a pivotal
technique to amplify model robustness. Yet, a considerable number of existing
methodologies lean heavily on heuristic foundations, rendering their intrinsic
mechanisms ambiguous. This manuscript takes both a theoretical and empirical
approach to understanding the phenomenon. Theoretically, we frame the discourse
around data augmentation within game theory's constructs. Venturing deeper, our
empirical evaluations dissect the intricate mechanisms of emblematic data
augmentation strategies, illuminating that these techniques primarily stimulate
mid- and high-order game interactions. Beyond the foundational exploration, our
experiments span multiple datasets and diverse augmentation techniques,
underscoring the universal applicability of our findings. Recognizing the vast
array of robustness metrics with intricate correlations, we unveil a
streamlined proxy. This proxy not only simplifies robustness assessment but
also offers invaluable insights, shedding light on the inherent dynamics of
model game interactions and their relation to overarching system robustness.
These insights provide a novel lens through which we can re-evaluate model
safety and robustness in visual recognition tasks.
