Research map / Adversarial robustness and security / Certified and empirical robustness
Certified Adversarial Robustness Under the Bounded Support Set
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace f-divergence iterative certification with closed-form W/TV certification objective | Gaussian and uniform smoothing on CIFAR-10 · CIFAR-10 test set | certification 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 certification | certified radius at cut-off: Gaussian radius about 50 times larger than uniform | Hurt |
| increase sample amount from 100 to 10000 (ablation) | Gaussian smoothing · CIFAR-10 certified accuracy | certified accuracy | No clear effect |
| increase sample amount from 100 to 10000 (ablation) | uniform smoothing measures · CIFAR-10 certified accuracy | certified accuracy: minor progress when samples are overly abundant | Helped |
| increase Gaussian sigma (smoothing variance) (ablation) | Gaussian smoothing · CIFAR-10 certified accuracy | initial certified accuracy vs robustness radius | Mixed |
Closest papers by meaning
- A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES (ICLR 2020 · similarity 0.93)
- Certified Adversarial Robustness via Randomized $\alpha$-Smoothing for Regression Models (NeurIPS 2024 · similarity 0.90)
- DRF: Improving Certified Robustness via Distributional Robustness Framework (AAAI 2024 · similarity 0.90)
- Probably Approximately Global Robustness Certification (ICML 2025 · similarity 0.89)
- Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing (NeurIPS 2024 · similarity 0.89)
- Incremental Randomized Smoothing Certification (ICLR 2024 · similarity 0.88)
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