@article{ART003277343},
author={Se-Young Jang and In-Jae Yoo and Byeong-Chan Park and Sun-Jip Kim and Seok-Yoon Kim and Young-Mo Kim},
title={A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI},
journal={ Journal of Software Forensics},
issn={3092-541X},
year={2025},
volume={21},
number={4},
pages={19-28},
doi={10.29056/jsav.2025.12.02}
TY - JOUR
AU - Se-Young Jang
AU - In-Jae Yoo
AU - Byeong-Chan Park
AU - Sun-Jip Kim
AU - Seok-Yoon Kim
AU - Young-Mo Kim
TI - A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI
JO - Journal of Software Forensics
PY - 2025
VL - 21
IS - 4
PB - Korea Software Assessment and Valuation Society
SP - 19
EP - 28
SN - 3092-541X
AB - With the expansion of open government data policies and the rapid deployment of Generative AI technologies, the utilization of KOGL (Korea Open Government License)-licensed open government works has significantly increased. However, copyright infringement issues continue to arise due to misunderstanding of license conditions, inaccurate labeling, and ambiguity in third-party ownership, leading to violations of attribution (BY), non-commercial use (NC), no-derivatives (ND), and false registration (CF). Traditional manual verification processes are insufficient for handling the growing volume and complexity of shared content, and they fail to automatically detect contextual and multimodal infringement patterns.
To address these challenges, this study proposes a multimodal AI-based copyright infringement risk prediction model that integrates textual descriptions and image content to evaluate KOGL compliance. The model consists of a six-stage pipeline-Input, Pre-processing, CUR Feature Extraction, GRU-based Sequence Modeling, CRU Rule Engine, and Final Decision-and learns contextual temporal patterns to classify infringement types. Through 1,000 repeated experiments, the proposed model achieved significantly improved performance over single-modal baselines in Accuracy, F1-Score, and AUC, and provides evidence-based explanatory outputs for practical decision support.
KW - Multimodal AI;KOGL Open Government Works;Copyright Infringement Risk Prediction;GRU Sequence Model;Open Government Data
DO - 10.29056/jsav.2025.12.02
ER -
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim and Young-Mo Kim. (2025). A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI. Journal of Software Forensics, 21(4), 19-28.
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim and Young-Mo Kim. 2025, "A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI", Journal of Software Forensics, vol.21, no.4 pp.19-28. Available from: doi:10.29056/jsav.2025.12.02
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim, Young-Mo Kim "A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI" Journal of Software Forensics 21.4 pp.19-28 (2025) : 19.
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim, Young-Mo Kim. A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI. 2025; 21(4), 19-28. Available from: doi:10.29056/jsav.2025.12.02
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim and Young-Mo Kim. "A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI" Journal of Software Forensics 21, no.4 (2025) : 19-28.doi: 10.29056/jsav.2025.12.02
Se-Young Jang; In-Jae Yoo; Byeong-Chan Park; Sun-Jip Kim; Seok-Yoon Kim; Young-Mo Kim. A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI. Journal of Software Forensics, 21(4), 19-28. doi: 10.29056/jsav.2025.12.02
Se-Young Jang; In-Jae Yoo; Byeong-Chan Park; Sun-Jip Kim; Seok-Yoon Kim; Young-Mo Kim. A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI. Journal of Software Forensics. 2025; 21(4) 19-28. doi: 10.29056/jsav.2025.12.02
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim, Young-Mo Kim. A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI. 2025; 21(4), 19-28. Available from: doi:10.29056/jsav.2025.12.02
Se-Young Jang, In-Jae Yoo, Byeong-Chan Park, Sun-Jip Kim, Seok-Yoon Kim and Young-Mo Kim. "A Copyright Infringement Risk Prediction Method for KOGL Open Government Works Using Multimodal AI" Journal of Software Forensics 21, no.4 (2025) : 19-28.doi: 10.29056/jsav.2025.12.02
