Modelling the Efficacy of Auto-Internet Warnings to Reduce Demand for Child Exploitation Materials

  • Paul A. WattersEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)


A number of proposals have been made over the years to implement notification systems to modify user behaviour, by sensitising users to the fact that their activities are not anonymous, and that further consequences may follow from future detections of illicit activity. While these systems can be automated to a large extent, there is a degree of manual processing required, so the cost-effectiveness and potential user coverage of such controls is critical. In this paper, we consider the problem of sensitising entrenched paedophiles who search for and download large amounts of Child Exploitation Material (CEM). Some countries, like New Zealand, operate a centralised censorship system which could be used to issue notifications when entrenched paedophiles search for CEM. We develop a statistical model to determine how many notices would need to be sent to entrenched paedophiles to ensure that they receive at least one notification over a 12 month period. The estimate of CEM viewers is based on actual data from the New Zealand internet filter. The modelling results indicate that sending 9,880 notices would result in entrenched paedophiles receiving at least one notice; for average CEM users, 53.27% of users would receive at least one notice within 12 months.


Child exploitation Cost benefit analysis Habituation 



This project is supported by DP160100601 “Automated internet warnings to prevent viewing of minor-adult sex images” - with J.Prichard, C. Spiranovic, T. Krone and R. Wortley. I would like to acknowledge the valuable feedback and contributions of Drs Prichard, Spiranovic, Krone and Wortley in the preparation of this manuscript.


  1. Alamar, B., Glantz, S.A.: Effect of increased social unacceptability of cigarette smoking on reduction in cigarette consumption. Am. J. Public Health 96(8), 1359–1363 (2006)CrossRefGoogle Scholar
  2. Alazab, M., Venkatraman, S., Watters, P.A., Alazab, M.: Zero-day malware detection based on supervised learning algorithms of API call signatures. In: Proceedings of the 9th Australian Data Mining Conference (2011)Google Scholar
  3. Bouton, M.E.: Learning and Behavior: A Contemporary Synthesis. Sunderland, Sinauer, MA (2007)Google Scholar
  4. Cale, J., Burton, M., Leclerc, B.: Primary prevention of child sexual abuse: applications, effectiveness, and international innovations. In: Winterdyk, J.A. (ed.) Crime Prevention: International Perspectives, Issues, and Trends, pp. 91–114. CRC Press, Boca Raton (2017)CrossRefGoogle Scholar
  5. Campbell, E.: Policing paedophilia: assembling bodies, spaces and things. Crime Media Cult. 12, 345–365 (2016). Scholar
  6. Choi, K., Boyle, R.G.: Changes in cigarette expenditure minimising strategies before and after a cigarette tax increase. Tob. Control 27(1), 99–104 (2017)CrossRefGoogle Scholar
  7. Clark, T.: Entrenched paedophiles: treating the untreatable. Aust. J. Forensic Sci. 24, 31–34 (1992)CrossRefGoogle Scholar
  8. Clarke, R.: Situational crime prevention. In: Wortley, R., Mazerolle, L. (eds.) Environmental Criminology and Crime Analysis. Willan Publishing, Cullompton (2008)Google Scholar
  9. Fraenkel, J., Wallen, N.: How to Design and Evaluate Research in Education. McGraw Hill, New York (2005)Google Scholar
  10. Ho, W.H., Watters, P.A.: Statistical and structural approaches to filtering internet pornography. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 4792–4798. IEEE (2004)Google Scholar
  11. Layton, R., Watters, P.A., Dazeley, R.: Automatically determining phishing campaigns using the USCAP methodology. In: Proceedings of the 5th APWG E-crime Research Summit (2010)Google Scholar
  12. Neisser, U.: Rising scores on intelligence tests. Am. Sci. 85, 440–447 (1997)Google Scholar
  13. Prichard, J., Spiranovic, C., Gelb, K., Watters, P.A., Krone, T.: Tertiary Education Students’ Attitudes to the harmfulness of viewing and distributing child pornography. Psychiatry Psychol. Law (ahead-of-print), 23(2), 224–239 (2015)Google Scholar
  14. Prichard, J., Watters, P., Spiranovic, C.: Internet subcultures and pathways to the use of child pornography. Comput. Law Secur. Rev. 27(6), 585–600 (2011)CrossRefGoogle Scholar
  15. Prichard, J., Spiranovic, C., Watters, P., Lueg, C.: Young people, child pornography, and subcultural norms on the internet. J. Am. Soc. Inf. Sci. Technol. 64(5), 992–1000 (2013)CrossRefGoogle Scholar
  16. Quayle, E.: Over the internet, under the radar: online child sexual abuse and exploitation–a brief (2017)Google Scholar
  17. Taylor, M., Quayle, E.: Criminogenic qualities of the internet in the collection and distribution of abuse images of children. Ir. J. Psychol. 29(1–2), 119–130 (2008)CrossRefGoogle Scholar
  18. Watters, P.A.: Why do users trust the wrong messages? A behavioural model of phishing. In: Proceedings of the 5th eCrime Researchers Summit, pp. 1–7 (2009).
  19. Westlake, B.G., Bouchard, M., Girodat, A.: How obvious is it? The content of child sexual exploitation websites. Deviant Behav. 38(3), 282–293 (2016)CrossRefGoogle Scholar
  20. Wolak, J., Finkelhor, D., Mitchell, K.J.: Trends in arrests for child pornography possession: the third national juvenile online victimization study (NJOV-3). Crimes against Children Research Center, University of New Hampshire, Durham, NH (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.La Trobe UniversityMelbourneAustralia

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