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Hard Examples Mining for Adversarial Attacks

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Communications, Signal Processing, and Systems (CSPS 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 654))

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Abstract

This paper focuses on adversarial attacks and security on machine learning and deep learning models. We apply different methods of perturbation on pictures in ImageNet and record the success rate of examples which are successfully attacked and wrongly recognized and then conclude a graph to describe the relationship between the intensity of attack and the accuracy of recognition. Then, we figure out the reason of examples hard to be attacked which is determined by models or determined by examples themselves. Besides, we analyze the pictures which are extremely defensive to the attacks and find out some visual characters to support them stay strong.

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Author information

Authors and Affiliations

  1. School of the Gifted Young, University of Science and Technology of China, Hefei, China

    Jiaxi Yang

Authors
  1. Jiaxi Yang
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Corresponding author

Correspondence to Jiaxi Yang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Dalian University of Technology, Dalian, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  5. Huazhong Agricultural University, Wuhan, China

    Xiaoxia Li

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Yang, J. (2021). Hard Examples Mining for Adversarial Attacks. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_112

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  • DOI: https://doi.org/10.1007/978-981-15-8411-4_112

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8410-7

  • Online ISBN: 978-981-15-8411-4

  • eBook Packages: EngineeringEngineering (R0)

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