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10/15/2021
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Membership inference attacks are a key measure to evaluate privacy leaka...
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10/11/2021
Parameterizing Activation Functions for Adversarial Robustness
Deep neural networks are known to be vulnerable to adversarially perturb...
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08/20/2021
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
The adversarial patch attack against image classification models aims to...
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08/16/2021
NeuraCrypt is not private
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sens...
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06/21/2021
Membership Inference on Word Embedding and Beyond
In the text processing context, most ML models are built on word embeddi...
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04/19/2021
Improving Adversarial Robustness Using Proxy Distributions
We focus on the use of proxy distributions, i.e., approximations of the ...
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01/26/2021
Property Inference From Poisoning
Property inference attacks consider an adversary who has access to the t...
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06/30/2020
Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds
Machine learning systems that rely on training data collected from untru...
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03/26/2020
Obliviousness Makes Poisoning Adversaries Weaker
Poisoning attacks have emerged as a significant security threat to machi...
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07/11/2019
Computational Concentration of Measure: Optimal Bounds, Reductions, and More
Product measures of dimension n are known to be concentrated in Hamming ...
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06/13/2019
Lower Bounds for Adversarially Robust PAC Learning
In this work, we initiate a formal study of probably approximately corre...
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05/29/2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Many recent works have shown that adversarial examples that fool classif...
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05/28/2019
Adversarially Robust Learning Could Leverage Computational Hardness
Over recent years, devising classification algorithms that are robust to...
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10/29/2018
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
We study adversarial perturbations when the instances are uniformly dist...
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10/02/2018
Can Adversarially Robust Learning Leverage Computational Hardness?
Making learners robust to adversarial perturbation at test time (i.e., e...
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09/10/2018
Multi-party Poisoning through Generalized p-Tampering
In a poisoning attack against a learning algorithm, an adversary tampers...
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09/09/2018
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
Many modern machine learning classifiers are shown to be vulnerable to a...
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11/10/2017