Research map / Adversarial robustness and security / Certified and empirical robustness
Certified Defenses against Adversarial Examples
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.
Closest papers by meaning
- Fast Training of Provably Robust Neural Networks by SingleProp (AAAI 2021 · similarity 0.91)
- Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples (ICML 2024 · similarity 0.90)
- Semidefinite relaxations for certifying robustness to adversarial examples (NeurIPS 2018 · similarity 0.89)
- Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity (NeurIPS 2022 · similarity 0.89)
- Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing (ICML 2025 · similarity 0.87)
- Learning Better Certified Models from Empirically-Robust Teachers (NeurIPS 2026 · similarity 0.87)
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.