It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.
@cite_0: The existence of large image datasets such as photos on the World Wide Web make it possible to build powerful generic models for low-level image attributes like color using simple histogram learning techniques. We describe the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labeled pixels. These classes exhibit a surprising degree of separability which we exploit by building a skin pixel detector that achieves an equal error rate of 88 . We compare the performance of histogram and mixture models in skin detection and find histogram models to be superior in accuracy and computational cost. Using aggregate features computed from the skin detector we build a remarkably effective detector for naked people. We believe this work is the most comprehensive and detailed exploration of skin color models to date.
@cite_6: We provide an overview of state-of-the-art approaches to visual adult image recognition which is a special case of one-class image classification. We present a representative selection of methods which we coarsely divide into three main groups. First we discuss color-based approaches which rely on the intuitive assumption that adult images usually feature skin-colored regions. Different ways of defining skin colors are described and example classification frameworks built on skin color models are presented. Another main group of approaches to adult image recognition is based on shape information which usually also exploit color information to find skin-colored regions of interest. Color and texture features are often used to augment such shape features. Finally we introduce approaches based on local feature descriptors.
@cite_3: 
@cite_11: We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system.
Historically, the most investigated feature was the human skin color @cite_0 . If the input image contains too much skin colored pixels, it was taken as an indicator of nudity. However, skin color solely is not reliable since a face closeup image has a lot of skin pixels while being non-porn. So, researchers augmented the skin color with other constraints or shape features @cite_3 . With the introduction of new computer vision models, porn detection algorithms became more accurate. For instance, introduced classification accuracy improvement by adopting the visual bag-of-words (BoW) model @cite_11 . BoW tries to extract the most common patches that exist on a set of training images. For a detailed survey of existing methods, please refer to @cite_6 .