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State-of-the-art defenses against adversarial patch attacks can now achievestrong certifiable robustness with a marginal drop in model utility. However,this impressive performance typically comes at the cost of 10-100x moreinference-time computation compared to undefended models -- the researchcommunity has witnessed an intense three-way trade-off between certifiablerobustness, model utility, and computation efficiency. In this paper, wepropose a defense framework named PatchCURE to approach this trade-off problem.PatchCURE provides sufficient "knobs" for tuning defense performance and allowsus to build a family of defenses: the most robust PatchCURE instance can matchthe performance of any existing state-of-the-art defense (without efficiencyconsiderations); the most efficient PatchCURE instance has similar inferenceefficiency as undefended models. Notably, PatchCURE achieves state-of-the-artrobustness and utility performance across all different efficiency levels,e.g., 16-23% absolute clean accuracy and certified robust accuracy advantagesover prior defenses when requiring computation efficiency to be close toundefended models. The family of PatchCURE defenses enables us to flexiblychoose appropriate defenses to satisfy given computation and/or utilityconstraints in practice.

PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses