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Proceedings Paper

Comparison of deep neural network fooling methods on the accuracy of classification
Author(s): Witold Oleszkiewicz
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Paper Abstract

The ability to train neural networks depends on access to data. In some areas, for example in medicine, it is difficult to obtain large datasets since medical data can contain very sensitive information. It is desirable to anonymize the dataset in such a way that the utility of machine learning prediction models is preserved. In this paper, we compare different methods of fooling deep neural networks. We investigate how different algorithms affects the accuracy of one classification task while fooling classifier in the other classification task.

Paper Details

Date Published: 1 October 2018
PDF: 6 pages
Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108082I (1 October 2018); doi: 10.1117/12.2501586
Show Author Affiliations
Witold Oleszkiewicz, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10808:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018
Ryszard S. Romaniuk; Maciej Linczuk, Editor(s)

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