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Supervised deep hashing for image content security

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Abstract

Due to the fast growth of image data on the web, it is necessary to ensure the content security of uploaded images. One of the fundamental problems behind this need is retrieving relevant images from the large-scale databases. Recently, hashing/binary coding algorithms have proved to be effective for large-scale visual information retrieval. Most existing hashing methods usually seek single linear projections to map each sample into a binary vector. In this paper, a supervised deep hashing method is proposed, which seeks multiple non-linear transformations to generate more discriminative binary codes with short bits. We implement a deep Convolutional Neural Network to achieve end-to-end hashing. A loss function is elaborately devised to preserve the similarity relationship between images, meanwhile minimize the quantization error and make hash bits distribute evenly. Extensive experimental comparisons with state-of-the-art hashing algorithms are conducted on CIFAR-10 and NUS-WIDE, the MAP reaches to 87.67% and 77.48% with 48 bits respectively. It shows that the proposed method achieves very competitive results with the state-of-the-arts.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (61771468), the Youth Innovation Promotion Association Chinese Academy of Sciences (2017209). Thanks to the contributions made by Yan Li from Beijing Kuaishou Technology Co., Ltd., who has examined the whole manuscript with respect to the usages of verb tense, singular and plural forms of nouns, and articles and has also conducted some experiments.

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    Authors and Affiliations

    1. School of Information and Electrical Engineering, Ludong University, Yantai, China

      Yanping Ma

    2. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong, China

      Dongbao Yang & Jian Yin

    3. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China

      Dongbao Yang & Hongtao Xie

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    1. Yanping Ma
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    2. Dongbao Yang
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    Correspondence to Dongbao Yang or Hongtao Xie.

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    Yanping Ma and Dongbao Yang contributed equally to this work and should be considered co-first authors.

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    Ma, Y., Yang, D., Xie, H. et al. Supervised deep hashing for image content security. Multimed Tools Appl 78, 661–676 (2019). https://doi.org/10.1007/s11042-017-5433-z

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