70 citations · 154 across the 8 of their papers we have counts for
3 papers · 1 filter
Group-based Robustness: A General Framework for Customized Robustness in the Real World
Weiran Lin, Keane Lucas, Neo Eyal +3
Machine-learning models are known to be vulnerable to evasion attacks that perturb model inputs to induce misclassifications. In this work, we identify real-world scenarios where t…
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
Keane Lucas, Matthew Jagielski, Florian Tramèr +2
It is becoming increasingly imperative to design robust ML defenses. However, recent work has found that many defenses that initially resist state-of-the-art attacks can be broken…
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Zhuoqun Huang, Neil G. Marchant, Keane Lucas +3
Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused…