90 citations · 149 across the 19 of their papers we have counts for
5 papers · 1 filter
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
Max Lennon, Nathan Drenkow, Philippe Burlina
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physical…
Adaptation and Generalization for Unknown Sensitive Factors of Variations
William Paul, Philippe Burlina
Assured AI in unrestricted settings is a critical problem. Our framework addresses AI assurance challenges lying at the intersection of domain adaptation, fairness, and counterfact…
AI Fairness via Domain Adaptation
Neil Joshi, Phil Burlina
While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployme…
Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods
William Paul, Yinzhi Cao, Miaomiao Zhang +1
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations…
Practical Blind Membership Inference Attack via Differential Comparisons
Bo Hui, Yuchen Yang, Haolin Yuan +3
Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. There ar…