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cs.LG2026
A Certified Robustness Framework for Backdoor Poisoning Attacks using -Divergences
Evan Rose, Alina Oprea
Machine learning systems trained on untrusted data are vulnerable to data and backdoor poisoning attacks, in which an adversary corrupts training and test samples to manipulate pre…
cs.LG2024
Fragile Giants: Understanding the Susceptibility of Models to Subpopulation Attacks
Isha Gupta, Hidde Lycklama, Emanuel Opel +2
As machine learning models become increasingly complex, concerns about their robustness and trustworthiness have become more pressing. A critical vulnerability of these models is d…
cs.LG2023
Understanding Variation in Subpopulation Susceptibility to Poisoning Attacks
Evan Rose, Fnu Suya, David Evans
Machine learning is susceptible to poisoning attacks, in which an attacker controls a small fraction of the training data and chooses that data with the goal of inducing some behav…