A new framework is introduced for end-to-end provable robustness against backdoor attacks by composing randomized smoothing with differentially private training via privacy profiles.
Intriguing properties of neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
AD-CERT uses logit-level adversarial distillation from a robust teacher combined with IBP to achieve state-of-the-art certified performance on robustness benchmarks, improving over feature-space distillation by up to 5.40 percentage points.
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
citing papers explorer
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Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
A new framework is introduced for end-to-end provable robustness against backdoor attacks by composing randomized smoothing with differentially private training via privacy profiles.
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Improving Certified Robustness via Adversarial Distillation
AD-CERT uses logit-level adversarial distillation from a robust teacher combined with IBP to achieve state-of-the-art certified performance on robustness benchmarks, improving over feature-space distillation by up to 5.40 percentage points.
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Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.