School of Computer and Information Science, Edith Cowan University, Perth, Western Australia
Before the introduction of the internet, the availability of child pornography was reported as on the decline (Jenkins 2001). Since its emergence, however, the internet has made child pornography a much more accessible and available means of trafficking across borders (Biegel 2001; Jenkins 2001; Wells, Finkelhor et al. 2007). The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. Likewise the internet is home to new developing social networks on the world wide web where young people are attracted to sharing their personal information amongst friends and family and inevitably become targets of predators. The volume and availability of such content, or targets for predators can be an overwhelming task for law enforcement to track and/or catalogue. In general cases image collections can range in the thousands (Taylor and Quayle 2003), and to assist in the identification and classification of child pornography within these large collections, the research of this author’s PhD seeks to establish a automated method of identifying and classifying material that has a high probability of being child pornography. This paper establishes the working progress of the author as they review existing relevant literature and hypothesise possible methods of identification and classification.