Secure Object Detection Based on Deep Learning

Citations

WEB OF SCIENCE

8
Citations

SCOPUS

13

초록

Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.

키워드

Deep Learning; Integrity; Object Detection; Privacy; Robustness; MEDICAL IMAGES; WATERMARKING; PRIVACY
제목
Secure Object Detection Based on Deep Learning
저자
Kim, Keonhyeong; Jung, Im Young
DOI
10.3745/JIPS.03.0161
발행일
2021-06
유형
Article
저널명
JIPS(Journal of Information Processing Systems)
권
17
호
3
페이지
571 ~ 585