생성형 AI 시대 저작권 보호를 위한 학습 불가능한 예제와 기술적 보호조치의 개선 방안 연구

A Study on the Improvement of Unlearnable Examples and Technical Protection Measures for Copyright Protection in the Generative AI Era
  • 김종성; 
  • 박성환; 
  • 황요한; 
  • 오병훈; 
  • 이재우; 
  • 외 1명

초록

This study analyzes the institutional and legal gaps that arise in the Copyright Act when generative AI systems learn from copyrighted works and other data without explicit authorization. Comprehensive measures are proposed to address these deficiencies. By conducting a comparative analysis on the copyright laws of major jurisdictions, including text and data mining (TDM) exceptions and recent case law, the study identifies critical limitations within the current domestic legal framework in regulating AI training practices. Furthermore, through empirical validation, the research examines the feasibility of the unlearnable examples technique as a practical protection measure to safeguard copyrighted materials during the AI learning process. Building on these insights, the study ultimately proposes complementary institutional and policy reforms designed to establish a balanced ecosystem that ensures both effective copyright protection and sustainable advancement of generative AI innovation.

키워드

Copyright Protection; Training Data; Technical Protection Measures; Artificial Intelligence; Unlearnable Examples; 저작권 보호; 학습데이터; 기술적 보호조치; AI; 학습 불가능한 예제
제목
생성형 AI 시대 저작권 보호를 위한 학습 불가능한 예제와 기술적 보호조치의 개선 방안 연구
제목 (타언어)
A Study on the Improvement of Unlearnable Examples and Technical Protection Measures for Copyright Protection in the Generative AI Era
저자
김종성; 박성환; 황요한; 오병훈; 이재우; 홍준호
DOI
10.9728/dcs.2025.26.12.3507
발행일
2025-12
유형
Y
저널명
디지털콘텐츠학회논문지
권
26
호
12
페이지
3507 ~ 3520

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