Appropriate Feature Selection and Post-processing for the Recognition of Artificial Pornographic Images in Social Networks

Abstract

Spreading and transmitting pornographic images over the Internet in the form of either real or artificial images is illegal and harmful to teenagers. Because traditional methods are primarily designed to identify real pornographic images, they are less efficient in dealing with artificial images. Therefore, a novel feature selection and post-processing method for the recognition of artificial pornographic images in social networks was proposed in the work. Firstly, features related to image size, skin color region, gray histogram, image color, edge density and direction, Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) were selected. Secondly, a post-processing process for these multiple feature was proposed, which includes two steps. The first step is feature expansion, which is aimed at improving the generalization ability of the recognition model. The other step is rapid feature extraction, which is aimed at reducing the time required for image recognition in social networks. Finally, experimental results demonstrate that the proposed method is effective for the recognition of artificial pornographic images in social networks.