activity
20192025
most citedMembership Model Inversion Attacks for Deep Networks

14 citations · 19 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2025★ 1 cited

WAVE-DETR Multi-Modal Visible and Acoustic Real-Life Drone Detector

Razvan Stefanescu, Ethan Oh, Ruben Vazquez +3

We introduce a multi-modal WAVE-DETR drone detector combining visible RGB and acoustic signals for robust real-life UAV object detection. Our approach fuses visual and acoustic fea…

cs.CV2022★ 4 cited

ReFace: Real-time Adversarial Attacks on Face Recognition Systems

Shehzeen Hussain, Todd Huster, Chris Mesterharm +5

Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an inpu…

cs.LG2019

Privacy Leakage Avoidance with Switching Ensembles

Rauf Izmailov, Peter Lin, Chris Mesterharm +1

We consider membership inference attacks, one of the main privacy issues in machine learning. These recently developed attacks have been proven successful in determining, with conf…

cs.LG2019★ 14 cited

Membership Model Inversion Attacks for Deep Networks

Samyadeep Basu, Rauf Izmailov, Chris Mesterharm

With the increasing adoption of AI, inherent security and privacy vulnerabilities formachine learning systems are being discovered. One such vulnerability makes itpossible for an a…

cs.LG2019

Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary

Chris Mesterharm, Rauf Izmailov, Scott Alexander +1

In this paper, we consider batch supervised learning where an adversary is allowed to corrupt instances with arbitrarily large noise. The adversary is allowed to corrupt any fe…