A System-level Perspective to Understand the Vulnerability of Deep Learning Systems

Tao Liu, Nuo Xu, Qi Liu, Yanzhi Wang, Wujie Wen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Deep neural network (DNN) is nowadays achieving the human-level performance on many machine learning applications like self-driving car, gaming and computer-aided diagnosis. However, recent studies show that such a promising technique has gradually become the major attack target, signiicantly threatening the safety of machine learning services. On one hand, the adversarial or poisoning attacks incurred by DNN algorithm vulnerabilities can cause the decision misleading with very high conidence. On the other hand, the system-level DNN attacks built upon models, training/inference algorithms and hardware and software in DNN execution, have also emerged for more diversiied damages like denial of service, private data stealing. In this paper, we present an overview of such emerging system-level DNN attacks by systematically formulating their attack routines. Several representative cases are selected in our study to summarize the characteristics of system-level DNN attacks. Based on our formulation, we further discuss the challenges and several possible techniques to mitigate such emerging system-level DNN attacks.

Original languageEnglish (US)
Title of host publicationASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages544-549
Number of pages6
ISBN (Electronic)9781450360074
DOIs
StatePublished - Jan 21 2019
Event24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 - Tokyo, Japan
Duration: Jan 21 2019Jan 24 2019

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
CountryJapan
CityTokyo
Period1/21/191/24/19

Keywords

  • DNN
  • Machine learning
  • Mitigation
  • Security
  • System-level

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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    Liu, T., Xu, N., Liu, Q., Wang, Y., & Wen, W. (2019). A System-level Perspective to Understand the Vulnerability of Deep Learning Systems. In ASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference (pp. 544-549). (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3287624.3288751