JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
TsCNNs-Based Inappropriate Image and Video Detection System for a Social Network |
| 영문제목(English Title) |
TsCNNs-Based Inappropriate Image and Video Detection System for a Social Network |
| 저자(Author) |
Youngsoo Kim
Taehong Kim
Seong-eun Yoo
|
| 원문수록처(Citation) |
VOL 18 NO. 05 PP. 0677 ~ 0687 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms. |
| 키워드(Keyword) |
CNN
Intelligent Image and Video Detection System
Tree-Structured Convolutional Neural Networks
TsCNNs
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| 파일첨부 |
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