ACS-Boot: Efficient Randomized Smoothing for Robustness Certification on Resource-Constrained Edge Devices

Randomized smoothing (RS) is a widely adopted certified defense offering provable robustness against adversarial attacks. Its plug-and-play nature, which requires only black-box access to the model, makes it well suited for protecting compressed and compiled models deployed on on-device platforms. However, its computational expense due to extensive Monte Carlo (MC) sampling limits practical deployment on resource-constrained platforms. We address this limitation by introducing the Agresti-Coull Style Studentized Bootstrap (ACS-Boot) interval, a computationally efficient alternative to the conventional Clopper-Pearson interval used in RS. ACS-Boot significantly reduces the sample complexity required to achieve comparable robustness guarantees, enabling certification at substantially lower cost. Experiments on MNIST, CIFAR-10, and ImageNet demonstrate that ACS-Boot maintains robust certification accuracy while cutting sample numbers by up to 20 times, achieving inference speed-ups of 20-24 times. Notably, under equal sampling budgets, ACS-Boot certifies up to 34% larger ℓ₂ perturbations, especially in low-sample regimes. We also provide theoretical insights into how the certified radius scales with sample size. Moreover, we validate ACS-Boot on edge and mobile devices, including Raspberry Pi and Google Pixel smartphones, achieving comparable certification accuracy with 10 times fewer samples and up to 10-fold faster inference, highlighting its real-world deployability.