Given the global challenges of security, both in physical and cyber worlds, security agencies must optimize the use of their limited resources. To that end, many security agencies have begun to use "security game" algorithms, which optimally plan defender allocations, using models of adversary behavior that have originated in behavioral game theory. To advance our understanding of adversary behavior, this paper presents results from a study involving an opportunistic crime security game (OSG), where human participants play as opportunistic adversaries against an algorithm that optimizes defender allocations. In contrast with previous work which often assumes homogeneous adversarial behavior, our work demonstrates that participants are naturally grouped into multiple distinct categories that share similar behaviors. We capture the observed adversarial behaviors in a set of diverse models from different research traditions, behavioral game theory, and Cognitive Science, illustrating the need for heterogeneity in adversarial models.