Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.
Showing 1–3 of 3
This paper provides a comprehensive structured analysis of adversarial robustness literature and introduces Auto-ART, an open-source framework that standardizes robustness evaluation across 50+ attacks, detects gradient masking, and maps compliance to regulatory frameworks. It addresses fragmentation in the field by combining meta-scientific synthesis with practical engineering tools.
This paper proposes a certifiably robust malware detection framework using randomized smoothing through feature ablation and noise injection to provide formal guarantees against adversarial evasion attacks. The approach achieves provable robustness without modifying the underlying ML classifier.
This paper introduces a GDP-based auditing framework that provides the first tight privacy audits for state-of-the-art differentially private synthetic data generators MST and AIM. The audits show a small gap between theoretical guarantees and empirical measurements in strong-privacy regimes.