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arXiv · 2005.12782

A Protection against the Extraction of Neural Network Models

Hervé Chabanne·Vincent Despiegel·Linda Guiga
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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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Hervé Chabanne, Vincent Despiegel, Linda Guiga. 2020-05-26. A Protection against the Extraction of Neural Network Models. https://arxiv.org/abs/2005.12782

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