Premium Access to Convolutional Neural Networks

被引：0

作者：

Bringer, Julien ^{[1
]}

Chabanne, Herve ^{[2
,3
]}

Guiga, Linda ^{[2
,3
]}

机构：

[1] Kallistech, Paris, France

[2] Idemia, Paris, France

[3] Telecom Paris, Paris, France

来源：

RISKS AND SECURITY OF INTERNET AND SYSTEMS (CRISIS 2020) | 2021年 / 12528卷

关键词：

Neural Networks; Software protection; Reverse engineering;

D O I：

10.1007/978-3-030-68887-5_13

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Neural Networks (NNs) are today used for all our daily tasks; for instance, in mobile phones. We here want to show how to restrict their access to privileged users. Our solution relies on a degraded implementation which can be corrected thanks to a PIN. We explain how to select a few parameters in an NN so as to maximize the gap in the accuracy between the premium and the degraded modes. We report experiments on an implementation of our proposal on a deep NN to prove its practicability.

引用

页码：219 / 234

页数：16

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