Pornographic Image Recognition By Strongly-Supervised Deep Multiple Instance Learning

In this paper, we propose a principled framework for pornographic image recognition. Specifically, we present our definition of pornographic images, which characterizes the pornographic contents in images as the exposure of private body parts. As the private body parts often lie in local image regions, we model each image as a bag of local image patches (instances), and assume that for each pornographic image at least one instance accounts for the pornographic content within it. This treatment allows us to cast the model training as a Multiple Instance Learning (MIL) problem. Furthermore, we propose a strongly-supervised setting for MIL by identifying the most likely pornographic instances in positive bags, which effectively prevents the algorithm from getting trapped in a bad local optima. Last but not least, we formulate our strongly -supervised MIL under the deep CNN framework to learn deep representations; hence we call it Strongly-supervised Deep MIL (SD-MIL). We demonstrate that our SD-MIL based system produces remarkable accuracy with 97.01% TPR at 1% FPR, testing on 117K pornographic images and 117K normal images from our newly-collected large scale dataset.