Pornography and child sexual abuse detection in image and video: a comparative evaluation

Abstract

With the growing amount of pornography content over Internet and cases of Child Sex Abuse (CSA) material possession and distribution, there is a rising demand for automatic detection of such content especially in certain environments such as educational or work places. The contribution of this paper is three fold. First, we present a critical review of automatic pornography and CSA detection in images and videos. Second, we provide an empirical evaluation of five selected pornography detection approaches representing traditional skin detection based as well as more recent deep learning based methods. The evaluations are performed under common criteria using two publicly available pornographic databases. Finally, we assess these methods on a dataset of real-world CSA material provided by Spanish Police Forces. This study observes that for pornography or CSA detection, the methods involving multiple features perform better than those using simple features like skin color or single image descriptor. It is also found that deep learning based methods outperform all of the other methods and report current state-of-the-art.