A Protection against the Extraction of Neural Network Models

Hervé Chabanne, Hervé Chabanne, Vincent Despiegel, Linda Guiga, Linda Guiga

Abstract

Given oracle access to a Neural Network (NN), it is possible to extract its underlying model. We here introduce a protection by adding parasitic layers which keep the underlying NN’s predictions mostly unchanged while complexifying the task of reverse-engineering. Our countermeasure relies on approximating a noisy identity mapping with a Convolutional NN. We explain why the introduction of new parasitic layers complexifies the attacks. We report experiments regarding the performance and the accuracy of the protected NN.

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Paper Citation


in Harvard Style

Chabanne H., Despiegel V. and Guiga L. (2021). A Protection against the Extraction of Neural Network Models.In Proceedings of the 7th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-491-6, pages 258-269. DOI: 10.5220/0010373302580269


in Bibtex Style

@conference{icissp21,
author={Hervé Chabanne and Vincent Despiegel and Linda Guiga},
title={A Protection against the Extraction of Neural Network Models},
booktitle={Proceedings of the 7th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2021},
pages={258-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010373302580269},
isbn={978-989-758-491-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - A Protection against the Extraction of Neural Network Models
SN - 978-989-758-491-6
AU - Chabanne H.
AU - Despiegel V.
AU - Guiga L.
PY - 2021
SP - 258
EP - 269
DO - 10.5220/0010373302580269