Adversarial examples: A survey
AbstractAdversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important. A survey of findings connected with adversarial examples is presented and discussed in this paper.
|Publication size in sheets||0.5|
|Book||Proceedings of the Baltic URSI Symposium supported by National Committees of the Baltic Countries, vol. CFP18N89-ART, 2018, IEEE, ISBN 978-83-949421-3-7, 300 p.|
|project||Defending machine learning models against adversarial examples. Project leader: Zawistowski Paweł,
, Phone: +48 22 2347718, start date 16-07-2018, planned end date 30-04-2019, II/2018/GD/2, Implemented
|Score|| = 15.0, BookChapterMatConf|
= 15.0, BookChapterMatConf
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