Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. (arXiv:2205.01992v1 [cs.LG])

The success of machine learning is fueled by the increasing availability of
computing power and large training datasets. The training data is used to learn
new models or update existing ones, assuming that it is sufficiently
representative of the data that will be encountered at test time. This
assumption is challenged by the threat of poisoning, an attack that manipulates
the training data to compromise the model's performance at test time. Although
poisoning has been acknowledged as a relevant threat …

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