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Image Firewall for Filtering Privacy or Sensitive Image Content Based on Joint Sparse Representation

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Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

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Abstract

As the commonest part of social networks, sharing an image in social networks transmits not only can provide more information, but also more intuitive than any text. However, images also can leak out information more easily than text, so the audit of image content is particularly essential. The disclosure of a tiny image, which involves sensitive information about individual, society even the state, may trigger a series of serious problems. In this paper, we design an image firewall to detect sensitive image content through joint sparse representation on features. We take LBP, SIFT and Wavelet features into consideration, trying to find an effective combination among these features. We also find some features, which have the same accuracy but less time cost. In addition, we consider the spatial relation of the detected objects, especially the distance between the persons appeared in an image. Experimental results show the effectiveness of the proposed methods.

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Notes

  1. 1.

    http://docs.opencv.org/2.4/modules/objdetect/doc/cascade_classification.html.

  2. 2.

    https://en.wikipedia.org/wiki/Edward_T._Hall.

  3. 3.

    http://www.vision.caltech.edu/Image_Datasets/Caltech101/.

  4. 4.

    http://www.vision.caltech.edu/Image_Datasets/Caltech256/.

  5. 5.

    http://docs.opencv.org/2.4/modules/objdetect/doc/cascade_classification.html.

  6. 6.

    https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61375047 & 61672203), National Innovation and Entrepreneurship Training Program project funding (No. 201510359033) in Hefei University of Technology, 2015. Our server is supported by Network Center of Hefei University of Technology.

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Authors and Affiliations

  1. School of Computer Science and Information, Hefei University of Technology, Hefei, 230009, China

    Zhan Wang, Ning Ling, Donghui Hu, Xiaoxia Hu, Tao Zhang & Zhong-qiu Zhao

Authors
  1. Zhan Wang
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  2. Ning Ling
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  3. Donghui Hu
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  4. Xiaoxia Hu
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  5. Tao Zhang
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  6. Zhong-qiu Zhao
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Corresponding author

Correspondence to Donghui Hu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

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Wang, Z., Ling, N., Hu, D., Hu, X., Zhang, T., Zhao, Zq. (2017). Image Firewall for Filtering Privacy or Sensitive Image Content Based on Joint Sparse Representation. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_48

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  • DOI: https://doi.org/10.1007/978-3-319-63315-2_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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