DevoX: Deep Voice Detection MLOps Framework for Crime Prevention in the Core-Edge Cloud

Yuri Seo, Hyeon Ki Jo, Seol Roh, Teh Jen Sun, Seung Woo Jeong, Hak Ho Kim, Thien Thu Ngo, Eui Nam Huh

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

Abstract

With the increase in criminal cases using deep voice, research related to deep voice detection is on the rise. However, the previous studies of deep voice detection are still insufficient when applied to actual crime prevention services. To address this issue, this paper proposes a new framework called "DevoX"that can apply deep voice detection to actual services. This framework continually trains on voice data from end devices (such as smartphones) for new types of deep voice crimes. Also, DevoX provides low-latency services through the core-edge cloud architecture. Edge cloud sends extracted features through encoding instead of call voice to prevent privacy leaks. It is possible to apply deep voice detection technology to actual services. Therefore, our study can be effectively utilized in preventing phishing based on deep voice.

Original languageEnglish
Title of host publication38th International Conference on Information Networking, ICOIN 2024
PublisherIEEE Computer Society
Pages678-682
Number of pages5
ISBN (Electronic)9798350330946
DOIs
Publication statusPublished - 2024
Event38th International Conference on Information Networking, ICOIN 2024 - Hybrid, Ho Chi Minh City, Viet Nam
Duration: 17 Jan 202419 Jan 2024

Publication series

NameInternational Conference on Information Networking
ISSN (Print)1976-7684

Conference

Conference38th International Conference on Information Networking, ICOIN 2024
Country/TerritoryViet Nam
CityHybrid, Ho Chi Minh City
Period17/01/2419/01/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • core-edge cloud
  • deep learning
  • deep voice
  • mlops

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