Research map / Adversarial robustness and security / Certified and empirical robustness
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
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 changed | Where | Measured | Effect |
|---|---|---|---|
| Use a classifier based on the active constraints of the dual sparse-reconstruction problem | union-of-subspaces classification · MNIST | certified robustness region | Helped |
| Combine the subspace-based classifier with Randomized Smoothing | union-of-subspaces classifier · MNIST | certified accuracy | Helped |
| Exploit concentration on small-volume regions | adversarially robust classification · theoretical classification setting | robust risk | Helped |
| Exploit low-dimensional union-of-subspaces structure | robust classifier · union-of-subspaces classification | certified perturbation region | Helped |
Closest papers by meaning
- Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness (NeurIPS 2019 · similarity 0.91)
- Adversarial examples from computational constraints (ICML 2019 · similarity 0.90)
- The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure (AAAI 2019 · similarity 0.90)
- Adversarial Examples Are Not Bugs, They Are Features (NeurIPS 2019 · similarity 0.90)
- Are adversarial examples inevitable? (ICLR 2019 · similarity 0.90)
- Adversarially Robust Generalization Requires More Data (NeurIPS 2018 · similarity 0.90)
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.