Secure Deep Learning Engineering: A Road Towards Quality Assurance of Intelligent Systems

Yang Liu, Lei Ma, Jianjun Zhao

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

Abstract

Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics. Deep neural networks are the key driving force behind its recent success, but still seem to be a magic black box lacking interpretability and understanding. This brings up many open safety and security issues with enormous and urgent demands on rigorous methodologies and engineering practice for quality enhancement. A plethora of studies have shown that state-of-the-art DL systems suffer from defects and vulnerabilities that can lead to severe loss and tragedies, especially when applied to real-world safety-critical applications. In this paper, we perform a large-scale study and construct a paper repository of 223 relevant works to the quality assurance, security, and interpretation of deep learning. Based on this, we, from a software quality assurance perspective, pinpoint challenges and future opportunities to facilitate drawing the attention of the software engineering community towards addressing the pressing industrial demand of secure intelligent systems.

Original languageEnglish
Title of host publicationFormal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Proceedings
EditorsYamine Ait-Ameur, Shengchao Qin
PublisherSpringer
Pages3-15
Number of pages13
ISBN (Print)9783030324087
DOIs
Publication statusPublished - Jan 1 2019
Event21st International Conference on Formal Engineering Methods, ICFEM 2019 - Shenzhen, China
Duration: Nov 5 2019Nov 9 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11852 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Formal Engineering Methods, ICFEM 2019
CountryChina
CityShenzhen
Period11/5/1911/9/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, Y., Ma, L., & Zhao, J. (2019). Secure Deep Learning Engineering: A Road Towards Quality Assurance of Intelligent Systems. In Y. Ait-Ameur, & S. Qin (Eds.), Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Proceedings (pp. 3-15). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11852 LNCS). Springer. https://doi.org/10.1007/978-3-030-32409-4_1