Challenges and Limitations Concerning Automatic Child Pornography Classification

Anton Moser, Marlies Rybnicek, Daniel Haslinger

2015

Abstract

The huge volume of data to be analyzed in the course of child pornography investigations puts special demands on tools and methods for automated classification, often used by law enforcement and prosecution. The need for a clear distinction between pornographic material and inoffensive pictures with a large amount of skin, like people wearing bikinis or underwear, causes problems. Manual evaluation carried out by humans tends to be impossible due to the sheer number of assets to be sighted. The main contribution of this paper is an overview of challenges and limitations encountered in the course of automated classification of image data. An introduction of state-of-the-art methods, including face- and skin tone detection, face- and texture recognition as well as craniofacial growth evaluation is provided. Based on a prototypical implementation of feasible and promising approaches, the performance is evaluated, as well as their abilities and shortcomings.

References

  1. Bokelberg (2015). Bokelberg. http://www.bokelberg.com/ DE/search/gallery/12783/10/1/ (last access: 09.01.2015).
  2. Colgan, P. (2011). Wikimedia commons. http://commons. wikimedia.org/wiki/ File:Bikini contest - black bikini.jpg? uselang=de (last access: 29.05.2014).
  3. Fu, Y., Guo, G., et al. (2010). Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11):1955- 1976.
  4. Geng, X., Yin, C., and Zhou, Z.-H. (2013). Facial age estimation by learning from label distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(10):2401-2412.
  5. Guo, G., Mu, G., Fu, Y., and Huang, T. S. (2009). Human age estimation using bio-inspired features. In IEEE Conference on Computer Vision and Pattern Recognition, pages 112-119.
  6. Hu, Z., Lin, X., and Yan, H. (2006). Torso detection in static images. In 8th International Conference on Signal Processing, volume 3. IEEE.
  7. Izadpanahi, S. and Toygar, O. (2012). Geometric feature based age classification using facial images. In IET Conference on Image Processing (IPR), pages 1-5.
  8. Jiang, Z., Yao, M., and Jiang, W. (2007). Skin detection using color, texture and space information. In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), volume 3, pages 366-370. IEEE.
  9. Karavarsamis, S., Ntarmos, N., Blekas, K., and Pitas, I. (2013). Detecting pornographic images by localizing skin rois. International Journal of Digital Crime and Forensics (IJDCF), 5(1):39-53.
  10. kinder.de (2015). Ihr Kind im 5. Lebensjahr. http:// www.kinder.de/themen/kleinkind/entwicklung/ artikel/ihr-kind-im-5-lebensjahr.html (last access: 09.01.2015).
  11. Li, W., Wang, Y., and Zhang, Z. (2012). A hierarchical framework for image-based human age estimation by weighted and ohranked sparse representation-based classification. In 5th IAPR International Conference on Biometrics (ICB), pages 19-25.
  12. MonsterMarketplace (2015). Picker-back bikini with metal ball studs. http://www.monstermarketplace.com/ SantaBanta.com (2015). Bikini. http:// www.santabanta.com/photos/bikini/14001216.htm? high=1 (last access: 09.01.2015).
  13. Santos, C., dos Santos, E. M., and Souto, E. (2012). Nudity detection based on image zoning. In 11th International Conference onInformation Science, Signal Processing and their Applications (ISSPA), pages 1098- 1103. IEEE.
  14. Selvi, V. T. and Vani, K. (2011). Age estimation system using mpca. In International Conference on Recent Trends in Information Technology (ICRTIT), pages 1055-1060. IEEE.
  15. Service, T. P. (2014). Project spade saves children. http://www.torontopolice.on.ca/modules.php? op=modload& name=News& file=article& sid=7171 (last access 31.10.2014).
  16. Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1701-1708. IEEE.
  17. Takimoto, H., Mitsukura, Y., Fukumi, M., and Akamatsu, N. (2006). A design of gender and age estimation system based on facial knowledge. In International Joint Conference (SICE-ICASE), pages 3883-3886.
  18. Talele, K. and Kadam, S. (2009). Face detection and geometric face normalization. In TENCON 2009-2009 IEEE Region 10 Conference, pages 1-6. IEEE.
  19. Tan, W. R., Chan, C. S., Yogarajah, P., and Condell, J. (2012). A fusion approach for efficient human skin detection. IEEE Transactions on Industrial Informatics, 8(1):138-147.
  20. Tanner, K. (2011). Modeling automated detection of children in images. Master's thesis, University of Rhode Island.
  21. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages I-511-I-518.
  22. Weda, H. and Barbieri, M. (2007). Automatic children detection in digital images. In IEEE International Conference on Multimedia and Expo, pages 1687-1690. IEEE.
  23. Yang, L., Li, H., Wu, X., Zhao, D., and Zhai, J. (2011). An algorithm of skin detection based on texture. In 4th International Congress on Image and Signal Processing (CISP), volume 4, pages 1822-1825.
  24. Yogarajah, P., Condell, J., Curran, K., McKevitt, P., and Cheddad, A. (2012). A dynamic threshold approach for skin tone detection in colour images. International Journal of Biometrics, 4(1):38-55.
  25. Zakaria, Z. and Suandi, S. A. (2011). Face detection using combination of neural network and adaboost. In TENCON 2011-2011 IEEE Region 10 Conference, pages 335-338. IEEE.
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Paper Citation


in Harvard Style

Moser A., Rybnicek M. and Haslinger D. (2015). Challenges and Limitations Concerning Automatic Child Pornography Classification . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 492-497. DOI: 10.5220/0005344904920497


in Bibtex Style

@conference{visapp15,
author={Anton Moser and Marlies Rybnicek and Daniel Haslinger},
title={Challenges and Limitations Concerning Automatic Child Pornography Classification},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={492-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005344904920497},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Challenges and Limitations Concerning Automatic Child Pornography Classification
SN - 978-989-758-090-1
AU - Moser A.
AU - Rybnicek M.
AU - Haslinger D.
PY - 2015
SP - 492
EP - 497
DO - 10.5220/0005344904920497