Label Sanitization against Label Flipping Poisoning Attacks

2 Mar 2018Andrea PaudiceLuis Muñoz-GonzálezEmil C. Lupu

Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way... (read more)

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