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.
초록·키워드
Abstract The rapid development of face forgery technology has raised concerns among researchers about information security. To mitigate the malicious abuse of the technology, deepfake detection has proven to be an effective countermeasure. However, existing methods only provide a true-false and binary classification, and they lack research on uncertainty estimation, which leads to the predictive reliability being unknown. The model uncertainty reflects its understanding of knowledge. Even when faced with unseen deepfake content, a detector without awareness of its cognitive limitations may still output incorrect predictions with high confidence. The detector assigns extremely high probabilities to samples, which is referred to a phenomenon known as overconfidence. In this paper, we introduce the novel concept of deepfake detection calibration, which focuses on aligning predicted probability with actual distribution to alleviate the overconfidence and estimate the uncertainty. Specifically, we introduce an innovative calibration method called packed-ensembles (PE), which divides a detector into multiple subnetworks. PE allows each subnetwork to focus on different forgery traces, resulting in reliable predictions without incurring additional computational costs. Comprehensive experiments demonstrate that our method effectively mitigates the overconfidence in deepfake detection while maintaining generalization, and significantly enhances detection reliability.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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