Machine Learning Security against Data Poisoning: Are We There Yet?. (arXiv:2204.05986v1 [cs.CR])

The recent success of machine learning has been fueled by the increasing
availability of computing power and large amounts of data in many different
applications. However, the trustworthiness of the resulting models can be
compromised when such data is maliciously manipulated to mislead the learning
process. In this article, we first review poisoning attacks that compromise the
training data used to learn machine-learning models, including attacks that aim
to reduce the overall performance, manipulate the predictions on specific test
samples, …

data data poisoning machine machine learning poisoning security