{"iri":"https://folio.openlegalstandard.org/RBMj5dbvLFgFGrPZ3MvFGYl","label":"Data Poisoning","sub_class_of":["https://folio.openlegalstandard.org/RBHMad8oNmYXkYHOHZLCgqv"],"parent_class_of":[],"is_defined_by":null,"see_also":[],"comment":null,"deprecated":false,"preferred_label":null,"alternative_labels":["Adversarial Data Manipulation","Dataset Compromise","Malicious Data Tampering","Poisoning Attack","Training Data Corruption"],"translations":{},"hidden_label":null,"definition":"Data Poisoning is a type of adversarial attack where malicious actors intentionally alter or manipulate the training data of a machine learning model to compromise its integrity and performance. This can lead to the model learning incorrect patterns or making erroneous predictions.","examples":[],"notes":[],"history_note":null,"editorial_note":null,"in_scheme":null,"identifier":null,"description":null,"source":null,"country":null}