A quantitative analysis framework for recurrent neural network

Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao

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

2 Citations (Scopus)

Abstract

Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in real-world applications. In this paper, we propose a quantitative analysis framework-DeepStellar-to pave the way for effective quality and security analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrial-grade RNN models, and extensible to develop customized analyzers and tools. We demonstrated that, with DeepStellar, users are able to design efficient test generation tools, and develop effective adversarial sample detectors. We tested the developed applications on three real RNN models, including speech recognition and image classification. DeepStellar outperforms existing approaches three hundred times in generating defect-triggering tests and achieves 97% accuracy in detecting adversarial attacks. A video demonstration which shows the main features of DeepStellar is available at: https://sites.google.com/view/deepstellar/tool-demo.

Original languageEnglish
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1062-1065
Number of pages4
ISBN (Electronic)9781728125084
DOIs
Publication statusPublished - Nov 2019
Event34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, United States
Duration: Nov 10 2019Nov 15 2019

Publication series

NameProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

Conference

Conference34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
Country/TerritoryUnited States
CitySan Diego
Period11/10/1911/15/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Control and Optimization

Fingerprint

Dive into the research topics of 'A quantitative analysis framework for recurrent neural network'. Together they form a unique fingerprint.

Cite this