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International Conference on Image and Graphics

ICIG 2021: Image and Graphics pp 765–776Cite as

Protecting Encrypted Video Stream Against Information Leak Using Adversarial Traces

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12890))

Abstract

TLS protocol encryption hides the specific packet content but not the network characteristics. The adversary can determine the relevant information of the video through traffic analysis by using convolutional neural networks. In this paper, we propose a new novel defense mechanism that injects dummy packets into the video stream by pre-computing adversarial traces. Moreover, to deal with different network conditions, we offer two adversarial methods. 1) When the network condition is relatively single, applying traditional adversarial examples will play a good role in the effect with a small amount of bandwidth overhead. 2) When the network condition becomes more complex, we have no prior knowledge about upcoming network packets. Therefore, the traditional adversarial sample method that needs entire stream traces to calculate cannot be applied. To solve this problem, we take advantage of adversarial patches’ input-agnostic and location-agnostic properties to generate “universal” adversarial traces. We experimentally demonstrate that our defense methods are promising solutions in the underlying scenarios.

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Acknowledgement

This work was partially supported by the National Natural Science Foundation of China NSFC [grant numbers 62072343, U1736211]. the National Key Research Development Program of China[grant numbers 2019QY(Y)0206].

Author information

Authors and Affiliations

  1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

    Ziwei Zhang & Dengpan Ye

Authors
  1. Ziwei Zhang
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  2. Dengpan Ye
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Corresponding author

Correspondence to Dengpan Ye .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Zhang, Z., Ye, D. (2021). Protecting Encrypted Video Stream Against Information Leak Using Adversarial Traces. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_62

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  • DOI: https://doi.org/10.1007/978-3-030-87361-5_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87360-8

  • Online ISBN: 978-3-030-87361-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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