How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?. (arXiv:2210.06516v1 [cs.CR])

Given the volume of data needed to train modern machine learning models,
external suppliers are increasingly used. However, incorporating external data
poses data poisoning risks, wherein attackers manipulate their data to degrade
model utility or integrity. Most poisoning defenses presume access to a set of
clean data (or base set). While this assumption has been taken for granted,
given the fast-growing research on stealthy poisoning attacks, a question
arises: can defenders really identify a clean subset within a contaminated
dataset …

arxiv clean data data