인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
With the rapid growth of second-hand trading platforms, fraudulent transactions have also increased significantly. However, existing fraud detection studies have primarily relied on structured or textual data, which limits their ability to capture the complex nature of fraud. This study proposes a multi-modal fraud detection model that integrates textual and visual information from second-hand trading posts collected from the Joonggonara platform. Text features were extracted using KLUE-BERT, while image features were obtained through the Swin Transformer. These embeddings were combined with structured data and input into an XGBoost classifier to predict fraudulent transactions. To address class imbalance, the SMOTE technique was applied to augment the minority (fraud) class. Experimental results demonstrate that the proposed multi-modal model, which combines structured, textual, and visual data, achieved the best overall performance compared to dual-modal models. These findings empirically confirm that integrating complementary information from text and images significantly enhances fraud detection capabilities. This study highlights the practical value of multi-modal approaches in second-hand transaction fraud detection and lays the groundwork for future research involving additional modalities and real-time detection systems.
본문·목차
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UCI(KEPA) : I410-151-25-02-094001107
