1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Evaluating Adversarial Robustness with Expected Viable Performance
Ryan McCoppin, Colin Dawson, Sean M. Kennedy +1
We introduce a metric for evaluating the robustness of a classifier, with particular attention to adversarial perturbations, in terms of expected functionality with respect to poss…
cs.LG2023★ 1 cited
Overcoming Adversarial Attacks for Human-in-the-Loop Applications
Ryan McCoppin, Marla Kennedy, Platon Lukyanenko +1
Including human analysis has the potential to positively affect the robustness of Deep Neural Networks and is relatively unexplored in the Adversarial Machine Learning literature.…