딥페이크 영상 학습을 위한 데이터셋 평가기준 개발
Development of Dataset Evaluation Criteria for Learning Deepfake Video
- 발행
- 2021 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 한밭대학교
- 출처
- 국내 KCI
- 원문
- 원문 보기 ↗
개념
키워드
Deepfake, Dataset, Video, Evaluation criteria, AHP, Deepfake, Dataset, Video, Evaluation criteria, AHP
초록
As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality.
 This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This stu