Multimodal approach for multimedia injurious contents blocking

  • Byeongtae Ahn
  • Seok-Woo JangEmail author


Due to the development of IT technology, harmful multimedia contents are spreading out. In addition, obscene and violent contents have a negative impact on children. Therefore, in this paper, we propose a multimodal approach for blocking obscene and violent video contents. Within this approach, there are two modules each detects obsceneness and violence. In the obsceneness module, there is a model that detects obsceneness based on adult and racy score. In the violence module, there are two models for detecting violence: one is the blood detection model using RGB region and the other is motion extraction model for observation that violent actions have larger magnitude and direction change. Through result of these three models, this approach judges whether or not the content is harmful. This can contribute to the blocking obscene and violent contents that are distributed indiscriminately.


Computer vision Obsceneness Violence Harmful contents Multimedia 



  1. 1.
    Chiu S-H, Liaw J-J (2005) An effective voting method for circle detection. Pattern Recogn Lett 26(2):121–133CrossRefGoogle Scholar
  2. 2.
    Clarin C, Dionisio J, Echavez M, Naval PC (2005) Dove: detection of movie violence using motion intensity analysis on skin and blood. Philippine Computing Science Congress 6:150–156Google Scholar
  3. 3.
    Fradi H, Luvison B, Pham QC (2017) Crowd behavior analysis using local mid-level visual descriptors. IEEE Transactions on Circuits and Systems for Video Technology 27(3):589–602. CrossRefGoogle Scholar
  4. 4.
    Gao Y, Liu H, Sun X, Wang C, Liu Y (2016) Violence detection using oriented violent flows. Image Vis Comput 49(5):37–41. CrossRefGoogle Scholar
  5. 5.
    He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916. CrossRefGoogle Scholar
  6. 6.
    Hu W, Wu O, Chen Z, Fu Z, Maybank S (2007) Recognition of pornographic web pages by classifying texts and images. IEEE Trans Pattern Anal Mach Intell 29(6):1019–1034. CrossRefGoogle Scholar
  7. 7.
    Keçeli AS, Kaya A (2017) Violent activity detection with transfer learning method. Lectronics Letters 53(15):1047–1048. CrossRefGoogle Scholar
  8. 8.
    Kim CY, Kwon OJ, Choi S (2011) A practical system for detecting obscene videos. IEEE Trans Consum Electron 57(2):646–650. CrossRefGoogle Scholar
  9. 9.
    Kim K, Kim U, Kwak S (2017) Read-time violence video detection based on movement change characteristics. Journal of Broadcast Engineering 22(2):234–239. CrossRefGoogle Scholar
  10. 10.
    Korea Press Foundation (2013) Survey of media audience. Korea Press Foundation, SeoulGoogle Scholar
  11. 11.
    Lee S, Shim W, Kim S (2009) Hierarchical system for objectionable video detection. IEEE Transactions on Consumer Electronic 55(2):667–684. CrossRefGoogle Scholar
  12. 12.
    Marmanis D, Datcu M, Esch T, Stilla U (2016) Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci Remote Sens Lett 13(1):105–109. CrossRefGoogle Scholar
  13. 13.
    Pereza M, Avilab S, Moreiraa D, Moraesa D, Testonic V, Valleb E, Goldensteina S, Rocha A (2017) Video pornography detection through deep learning techniques and motion information. Neurocomputing 230(4):279–293. CrossRefGoogle Scholar
  14. 14.
    Senst T, Eiselein V, Kuhn A, Sikora T (2017) Crowd violence detection using global motioncompensated lagrangian features and scale-sensitive video-level representation. IEEE Transactions on Information Forensics and Security 12(12):2945–2956. CrossRefGoogle Scholar
  15. 15.
    Zheng H, Liu H, Daoudi M (2004) Blocking objectionable images: adult images and harmful symbols. In Proceedings of the IEEE International Conf. on Multimedia and ExpoGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Liberal and Arts CollegeAnyang UniversityAnyangSouth Korea
  2. 2.Department of SoftwareAnyang UniversityAnyangSouth Korea

Personalised recommendations