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Open Access

DeepSI: A Sensitive-Driven Testing Samples Generation Method of Whitebox CNN Model for Edge Computing

School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract

In recent years, Deep Learning (DL) technique has been widely used in Internet of Things (IoT) and Industrial Internet of Things (IIoT) for edge computing, and achieved good performances. But more and more studies have shown the vulnerability of neural networks. So, it is important to test the robustness and vulnerability of neural networks. More specifically, inspired by layer-wise relevance propagation and neural network verification, we propose a novel measurement of sensitive neurons and important neurons, and propose a novel neuron coverage criterion for robustness testing. Based on the novel criterion, we design a novel testing sample generation method, named DeepSI, which involves definitions of sensitive neurons and important neurons. Furthermore, we construct sensitive-decision paths of the neural network through selecting sensitive neurons and important neurons. Finally, we verify our idea by setting up several experiments, then results show our proposed method achieves superior performances.

Keywords

neuron sensitivityLayer-wise Relevance Propagation (LRP)neural network verificationdeep learning testing

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Tsinghua Science and Technology
Volume 29 Issue 3,
June 2024
Pages 784-794
DOI: 10.26599/TST.2023.9010057
Cite this article:
Lian Z, Tian F. DeepSI: A Sensitive-Driven Testing Samples Generation Method of Whitebox CNN Model for Edge Computing. Tsinghua Science and Technology, 2024, 29(3): 784-794. https://doi.org/10.26599/TST.2023.9010057

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Received: 03 February 2023
Revised: 11 May 2023
Accepted: 02 June 2023
Published: 04 December 2023
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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