A quantitative analysis framework for recurrent neural network

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1062-1065
ページ数4
ISBN(電子版)9781728125084
DOI
出版ステータス出版済み - 11 2019
イベント34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, 米国
継続期間: 11 10 201911 15 2019

出版物シリーズ

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

会議

会議34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
Country米国
CitySan Diego
Period11/10/1911/15/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Control and Optimization

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