Real-time detection of children's skin on social networking sites using Markov random field modelling

Authors:
Mofakharul Islam;Paul A. Watters;John Yearwood
Affiliations:
Internet Commerce Security Laboratory, University of Ballarat, Ballarat, Australia;Internet Commerce Security Laboratory, University of Ballarat, Ballarat, Australia;Internet Commerce Security Laboratory, University of Ballarat, Ballarat, Australia
Venue:
Information Security Tech. Report
Year:
2011

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Abstract

Social networking sites are increasingly being used as the source for paedophiles to search for, download and exchange child exploitation images. Law Enforcement Agencies (LEAs) around the world face a difficult challenge to combat technologically-savvy paedophiles. In this paper, we propose a framework for detecting images containing children's pictures in different poses, with the ultimate view of identifying and classifying images as corresponding to the COPINE scale. To achieve the goal of automatic detection, we present a novel stochastic vision model based on a Markov Random Fields (MRF) prior, which will employ a skin model and human affine-invariant geometric descriptor to detect and identify skin regions containing pornographic contexts.