13 citations · 15 across the 4 of their papers we have counts for
4 papers
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Marius Arvinte, Cory Cornelius, Jason Martin +1
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the tr…
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
ShengYun Peng, Weilin Xu, Cory Cornelius +6
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a sui…
RobArch: Designing Robust Architectures against Adversarial Attacks
ShengYun Peng, Weilin Xu, Cory Cornelius +4
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the a…
Synthetic Dataset Generation for Adversarial Machine Learning Research
Xiruo Liu, Shibani Singh, Cory Cornelius +4
Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets. This construction of adversarial examples is not realis…