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Privacy Sensitive Large-Margin Model for Face De-Identification

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Neural Computing for Advanced Applications (NCAA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1265))

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Abstract

There is an increasing concern of face privacy protection along with the wide application of big media data and social networks due to free online data release. Although some pioneering works obtained some achievements, they are not sufficient enough for sanitizing the sensitive identity information. In this paper, we propose a generative approach to de-identify face images yet preserving the non-sensitive information for data reusability. To ensure a high privacy level, we introduce a large-margin model for the synthesized new identities by keeping a safe distance with both the input identity and existing identities. Besides, we show that our face de-identification operation follows the \(\epsilon \)-differential privacy rule which can provide a rigorous privacy notion in theory. We evaluate the proposed approach using the vggface dataset and compare with several state-of-the-art methods. The results show that our approach outperforms previous solutions for effective face privacy protection while preserving the major utilities.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61806063, 61772161, 61622205. The authors would like to thank the reviewers who have provided insightful comments and valuable suggestions.

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Authors and Affiliations

  1. Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zhiqiang Guo, Huigui Liu & Zhenzhong Kuang

  2. Osaka University, Osaka, Japan

    Yuta Nakashima & Noboru Babaguchi

Authors
  1. Zhiqiang Guo
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  2. Huigui Liu
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  3. Zhenzhong Kuang
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  4. Yuta Nakashima
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  5. Noboru Babaguchi
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Corresponding author

Correspondence to Zhenzhong Kuang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Hefei University of Technology, Hefei, China

    Zhao Zhang

  3. Chongqing University, Chongqing, China

    Zhou Wu

  4. South China Normal University, Guangzhou, China

    Tianyong Hao

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Guo, Z., Liu, H., Kuang, Z., Nakashima, Y., Babaguchi, N. (2020). Privacy Sensitive Large-Margin Model for Face De-Identification. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_40

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  • DOI: https://doi.org/10.1007/978-981-15-7670-6_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7669-0

  • Online ISBN: 978-981-15-7670-6

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