Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks
Traditional conformal prediction (CP) fails under adversarial attacks, while existing robust CP methods suffer from excessively large prediction sets or high computational overhead on large-scale tasks. To address this, we propose Lip-RCP—the first efficient robust prediction framework that deeply integrates 1-Lipschitz robust neural networks with CP. Methodologically, we impose Lipschitz constraints to ensure output stability and derive, for the first time, a theoretical worst-case coverage bound for standard CP under arbitrary attack magnitudes. Experiments on medium- and large-scale benchmarks (e.g., ImageNet) show that Lip-RCP reduces robust prediction set size by up to 42% over state-of-the-art methods while accelerating inference by 3.8×. Crucially, it strictly guarantees both nominal coverage ≥90% and finite-sample robust coverage—without compromising statistical validity.