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Adversarial Machine Learning

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Machine learning models, including neural networks, have been shown to be vulnerable to malicious inputs designed to compromise their integrity. These adversarial examples manipulate system behaviours in order to cause undesirable outputs.

This talk will discuss this problem, its ramifications, an explantation of how these adversarial examples are generated and an overview of the methods used to try and fight this problem.

This talk is part of the Churchill CompSci Talks series.

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