학술논문

Discovery of Elsagate: Detection of Sparse Inappropriate Content from Kids Videos
Document Type
Conference
Source
2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Consumer Technologies Conference (ZINC), 2020 Zooming Innovation in. :46-47 May, 2020
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Videos
YouTube
Superluminescent diodes
Mathematical model
Data models
Detectors
Convolutional neural networks
Big Data security
Elsagate
inappropriate video detection
Sparse Linear Discrimination
Language
Abstract
Elsagate refers to kids videos containing inappropriate content for children but difficult to filter thus often shown up on kids channels. Most of the mainstream kids channels have powerful filters. However, when the inappropriate content is sparse, the filters often fail to detect the inappropriateness. In this paper, we introduce our work in progress, a scheme to detect Elsagate videos based on Sparse Linear Discrimination (SLD), an effective way to help detect and classify these kinds of unsafe videos and enrich better user experience.