Conformal prediction is a powerful tool to generate uncertainty sets with
guaranteed coverage using any predictive model, under the assumption that the
training and test data are i.i.d.. Recently, it has been shown that adversarial
examples are able to manipulate conformal methods to construct prediction sets
with invalid coverage rates, as the i.i.d. assumption is violated. To address
this issue, a recent work, Randomized Smoothed Conformal Prediction (RSCP), was
first proposed to certify the robustness of conformal prediction methods to
adversarial noise. However, RSCP has two major limitations: (i) its robustness
guarantee is flawed when used in practice and (ii) it tends to produce large
uncertainty sets. To address these limitations, we first propose a novel
framework called RSCP+ to provide provable robustness guarantee in evaluation,
which fixes the issues in the original RSCP method. Next, we propose two novel
methods, Post-Training Transformation (PTT) and Robust Conformal Training
(RCT), to effectively reduce prediction set size with little computation
overhead. Experimental results in CIFAR10, CIFAR100, and ImageNet suggest the
baseline method only yields trivial predictions including full label set, while
our methods could boost the efficiency by up to $4.36\times$, $5.46\times$, and
$16.9\times$ respectively and provide practical robustness guarantee. Our codes
are available at
this https URL

该研究提出了一种名为 RSCP + 的新框架，用于在评估中提供可靠的鲁棒性保证，同时提出了两种新的方法 PTT 和 RCT，以减少预测集大小并提供实际的鲁棒性保证。