Machine Learning Security against Data Poisoning: Are We There Yet?

arXiv:2204.05986v3 Announce Type: replace-cross
Abstract: The recent success of machine learning (ML) 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 ML models, including attacks that aim to reduce the overall performance, manipulate …

abstract applications arxiv availability computing computing power cs.cr cs.cv data data poisoning however machine machine learning power security success type