atlas

Research map / Adversarial robustness and security / Certified and empirical robustness

Certified Adversarial Robustness Under the Bounded Support Set

ICML 2022 · Adversarial robustness and security › Certified and empirical robustness · approach: Certified training and ensembles

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace f-divergence iterative certification with closed-form W/TV certification objectiveGaussian and uniform smoothing on CIFAR-10 · CIFAR-10 test setcertification runtime: 10 mins vs 90 mins (20 steps) and 3 hours (50 steps)Helped
use uniform smoothing on l2 or l-infinity ball instead of Gaussian (ablation)uniform smoothing measures · CIFAR-10 l2 certificationcertified radius at cut-off: Gaussian radius about 50 times larger than uniformHurt
increase sample amount from 100 to 10000 (ablation)Gaussian smoothing · CIFAR-10 certified accuracycertified accuracyNo clear effect
increase sample amount from 100 to 10000 (ablation)uniform smoothing measures · CIFAR-10 certified accuracycertified accuracy: minor progress when samples are overly abundantHelped
increase Gaussian sigma (smoothing variance) (ablation)Gaussian smoothing · CIFAR-10 certified accuracyinitial certified accuracy vs robustness radiusMixed

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy