Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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TsCNNs-Based Inappropriate Image and Video Detection System for a Social Network

  • Kim, Youngsoo (Dept. of Artificial Intelligence, Jeonju University) ;
  • Kim, Taehong (School of Information and Communication Engineering, Chungbuk National University) ;
  • Yoo, Seong-eun (School of Artificial Intelligence, Daegu University)
  • Received : 2021.11.06
  • Accepted : 2022.03.13
  • Published : 2022.10.31
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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.

Keywords

Acknowledgement

This research was supported by the Daegu University Research Grant, 2018.

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