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

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Advances in Services Computing (APSCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10065))

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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.

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Acknowledgments

This study is supported by the China Postdoctoral Science Foundation (2016M592450), and the Hunan Provincial Natural Science Foundation of China (2016JJ4119).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Fangfang Li, Siwei Luo & Xiyao Liu

  2. Information Security and Big Data Research Institute, Central South University, Changsha, 410083, Hunan, China

    Jianbin Li

Authors
  1. Fangfang Li
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  2. Siwei Luo
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  3. Xiyao Liu
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  4. Jianbin Li
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Corresponding author

Correspondence to Jianbin Li .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. North China University of Technology, Beijing, China

    Yanbo Han

  3. University of Murcia, Murcia, Spain

    Gregorio Martínez Pérez

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Li, F., Luo, S., Liu, X., Li, J. (2016). Appropriate Feature Selection and Post-processing for the Recognition of Artificial Pornographic Images in Social Networks. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-49178-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49177-6

  • Online ISBN: 978-3-319-49178-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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