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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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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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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