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
Image and video forgeries are becoming more advanced with the latest developments of artificial intelligence and other digital editing software that have highly developed and take the artificial intelligence and the digital editing software to another level that becomes impossible to identify without the need to intervene in the data. This becomes a serious challenge to the forensic investigation department, journalism and cyber security. Although they are very popular, traditional convolution neural networks (CNNs) have serious limitations in identifying the most delicate manipulations because they do not allow the preservation of spatial hierarchies. To fill in this hole, we introduce a hybrid tamper detection system based on combining Capsule Networks and Error Level Analysis (ELA). CapsNet preserves spatial relation to detect the fine-grained anomalous areas, whereas ELA focuses on non uniformities throughout the compression artifacts to determine the areas where tampering has occurred. The suggested approach was tested on the common forensic datasets and showed higher accuracy and resilience than conventional CNN-based models in both the detection of image and video forgery. The study presents a stable and understandable method of authenticating digital media that can be used to provide a robust defense against misinformation, cybercrime, and deepfake filtering.
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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