Journal of Platform Technology

ICT Platform Society (ICT플랫폼학회)
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Annotation Method for Reliable Video Data

신뢰성 영상자료를 위한 어노테이션 기법

  • Received : 2023.12.12
  • Accepted : 2024.01.02
  • Published : 2024.02.28
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Abstract

With the recent increase in the use of artificial intelligence, AI TRiSM data management within organizations has become important, and thus securing data reliability has emerged as an essential requirement for data-based decision-making. Digital content is transmitted through the unreliable Internet to the cloud where the digital content storage is located, then used in various applications. When detecting anomaly of data, it is difficult to provide a function to check content modification due to its damage in digital content systems. In this paper, we design a technique to guarantee the reliability of video data by expanding the function of data annotation. The designed annotation technique constitutes a prototype based on gRPC to handle a request and a response in a webUI that generates classification label and Merkle tree of given video data.

최근 인공지능 활용 증가로 조직 내부의 AI TRiSM 보장 데이터 관리가 중요해지고 있으며, 이에 따라 데이터 신뢰성 확보는 데이터 기반 의사결정의 필수 요구사항으로 등장하였다. 생성된 디지털 콘텐츠는 신뢰성을 갖지 않는 인터넷을 통해 디지털 콘텐츠 저장소가 위치한 클라우드에 전송되어 다양하게 활용된다. 그러나 기존의 디지털 콘텐츠 시스템은 자료훼손에 따른 내용 수정을 확인하는 데이터 이상감지 기능을 제공하기 쉽지 않다. 이 논문에서는 데이터 어노테이션의 기능 확장을 통해 영상데이터의 신뢰성을 보증하기 위한 기법을 설계한다. 설계된 어노테이션 기법은 webUI 방식으로 gRPC 기반 요청 및 응답을 처리할 수 있도록 프로토타입을 구성하여 주어진 영상의 분류 레이블 및 머클트리를 생성한다.

Keywords

Acknowledgement

본 논문은 중소벤처중소벤처기업부(중소기업기술정보진흥원, RS-2023-00225234) 2023년도 산학연 CollaboR&D 사업의 산업현장의 디지털 영상데이터의 AI 기반 무결성 및 검증 기술개발과제의 지원을 받아 수행된 연구임

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