activity
20132020
most citedMembership Model Inversion Attacks for Deep Networks

14 citations · 29 across the 6 of their papers we have counts for

collaborators

8 papers

cs.LG2020★ 3 cited

Minimax Defense against Gradient-based Adversarial Attacks

Blerta Lindqvist, Rauf Izmailov

State-of-the-art adversarial attacks are aimed at neural network classifiers. By default, neural networks use gradient descent to minimize their loss function. The gradient of a cl…

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…

cs.LG2018★ 4 cited

AutoGAN: Robust Classifier Against Adversarial Attacks

Blerta Lindqvist, Shridatt Sugrim, Rauf Izmailov

Classifiers fail to classify correctly input images that have been purposefully and imperceptibly perturbed to cause misclassification. This susceptability has been shown to be con…

cs.CR2016

Detection under Privileged Information

Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov +4

For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network…

cs.CG2014

Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications

Paul Bendich, Ellen Gasparovic, John Harer +2

We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both g…

stat.ML2013★ 8 cited

Constructive Setting of the Density Ratio Estimation Problem and its Rigorous Solution

Vladimir Vapnik, Igor Braga, Rauf Izmailov

We introduce a general constructive setting of the density ratio estimation problem as a solution of a (multidimensional) integral equation. In this equation, not only its right ha…