Robust and Accurate -- Compositional Architectures for Randomized Smoothing

Understand

Randomized Smoothing (RS) is considered the state-of-the-art approach to obtain certifiably robust models for challenging tasks.

• However, current RS approaches drastically decrease standard accuracy on unperturbed data, severely limiting their real-world utility.
• To address this limitation, we propose a compositional architecture, ACES, which certifiably decides on a per-sample basis whether to use a smoothed model yielding predictions with guarantees or a more accurate standard model without guarantees.
• This, in contrast to prior approaches, enables both high standard accuracies and significant provable robustness.