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Deep learning based scene text reading for cloud audit information extraction

  • Hezhong Pan
  • , Chuanyi Liu*
  • , Shaoming Duan
  • , Peiyi Han
  • , Xinyi Zhang
  • , Binxing Fang
  • *Corresponding author for this work
  • Beijing University of Posts and Telecommunications
  • Harbin Institute of Technology Shenzhen
  • The Chinese University of Hong Kong, Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

User behavior auditing is one of the powerful means to cope with internal threats in cloud computing and the key component for user behavior auditing is information extraction. The authors in this paper present a method, which automatically extract audit information from session image transported by remote desktop protocol. This method is realized by deep learning based scene text reading. To address the challenges of applying scene text reading model in cloud audit system, the authors modified the structure of aforementioned model, attached a pre-process to it and presented a synthetic data generation method for training models. The evaluation part testifies that this information extraction method could attain high accuracy rate of 93.4 % on the real scene data.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 4th International Conference on Data Science in Cyberspace, DSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages686-693
Number of pages8
ISBN (Electronic)9781728145280
DOIs
StatePublished - Jun 2019
Externally publishedYes
Event4th IEEE International Conference on Data Science in Cyberspace, DSC 2019 - Hangzhou, China
Duration: 23 Jun 201925 Jun 2019

Publication series

NameProceedings - 2019 IEEE 4th International Conference on Data Science in Cyberspace, DSC 2019
Volume2019-January

Conference

Conference4th IEEE International Conference on Data Science in Cyberspace, DSC 2019
Country/TerritoryChina
CityHangzhou
Period23/06/1925/06/19

Keywords

  • Cloud audit
  • Scene text reading
  • Synthetic data generation
  • Text detection
  • Text recognition

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