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Found 8 papers, 0 papers with code
Minimax Defense against Gradient-based Adversarial Attacks
Our Minimax adversarial approach presents a significant shift in defense strategy for neural network classifiers.
Privacy Leakage Avoidance with Switching Ensembles
We consider membership inference attacks, one of the main privacy issues in machine learning.
Membership Model Inversion Attacks for Deep Networks
With the increasing adoption of AI, inherent security and privacy vulnerabilities formachine learning systems are being discovered.
Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary
In this paper, we consider batch supervised learning where an adversary is allowed to corrupt instances with arbitrarily large noise.
AutoGAN: Robust Classifier Against Adversarial Attacks
For different magnitudes of perturbation in training and testing, AutoGAN can surpass the accuracy of FGSM method by up to 25\% points on samples perturbed using FGSM.
Detection under Privileged Information
In this paper, we consider an alternate learning approach that trains models using "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems.
Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications
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.
Constructive Setting of the Density Ratio Estimation Problem and its Rigorous Solution
We introduce a general constructive setting of the density ratio estimation problem as a solution of a (multidimensional) integral equation.
