Child pornography is an increasingly visible problem in society today. Methods currently employed to combat it may be considered primitive and inefficient, and legal and technical issues can exacerbate the problem significantly. We propose a network-based de tection system that us es a stochastic weak estimator coupled with a linear classifier, which is a ppropriate in this context due to the non-stationarity of the input data. Our experiments show that the sy stem is capable of distinguishing child pornography images from non-child pornography images even when the obscene image is redu ced to only 20% of its representation. This method for identifying offensive material is potentially attractive to law enforcement and can be accomplished with acceptable overhead. We believe our approach, with minor adaptations, is of independent interest for use in a number of network applications which benefit from packet classification beyond detecting child pornography. These include securi ty applications such as detecting malicious packets, and network anom alies consisting of dangerous tra ffic fluctuations, abusive use of certain services, and distributed denial-of-service attacks.