The vast increase in the use of social networks and other internet-based communication tools contributed to the escalation of the problem of exchanging child pornographic material over the internet. The problem of dissemination of child pornographic material could be addressed using dedicated image detection algorithms capable of rating the inappropriateness level of images exchanged through computer networks so that images with inappropriate content involving children are blocked. However, the complexity of the image detection task coupled with the nonexistence of suitable datasets, inhibit the development of efficient algorithms that can be used for detecting offensive images containing children. To deal with the problem, we propose a methodological approach that can be used for supporting the development of child pornography detectors through the generation of synthetic datasets and through the decomposition of the task into a set of simpler tasks for which training data is available. Preliminary results show the promise of the proposed approach.