Illegal image classification based on ensemble deep model
ZHANG Chen
DU Gang
DU Xuetao
Abstract：The rapid development of mobile communication technology has greatly promoted the communication experience of users.How to identify and filter out the illegal content in a large a-mount of data is crucial for improving the ability and level of illegal information management in China Mobile.Towards this end,this paper proposes an ensemble deep model (EDM)to classify illegal images.In this approach,several deep models with diverse network structures and comple-mentary information are integrated by using the proposed scheme,and the illegal images with di-verse distributions will be distinguished.To evaluate the effectiveness of the proposed approach, we first collect and set up an illegal image dataset,and compare the proposed approach with the traditional support vector machine(SVM)based image classification method and Alexnet-based, VGG-based and Googlenet-based methods.Experiments show that the proposed approach clearly outperforms the existing methods and obtains excellent classification performance in accurate (94％),precision (84％)and recall (98％).
Keywords：image classificationillegal image detectiondeep learningSVM classifier
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 21-26 )