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Enhancing Adversarial Examples with Flip-Invariance and Brightness-Invariance

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Security and Privacy in Digital Economy (SPDE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1268))

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Abstract

Despite of achieving remarkable success in computer vision tasks, convolutional neural networks still face the threat of adversarial examples, crafted by adding small human-invisible perturbations on clean inputs. Usually, most of existing black-box adversarial attacks show extremely low transferability while encountering powerful defense models. In this paper, based on the observed invariant property of convolutional neural networks (i.e., the models could maintain accuracy to transformed images), we propose two new methods to improve the transferability which are called as the flip-invariant attack method (FIM) and the brightness-invariant attack method (BIM), respectively. Both the novel approaches derive multiple different logit outputs by inputting the transformed copies of the original image into the white-box model. Simultaneously, the ensemble of these outputs is attacked to avoid overfitting the white-box model and generating more transferable adversarial examples. Moreover, the newly-proposed FIM and BIM methods can be naturally combined with other gradient-based methods. Extensive experiments on the ImageNet dataset prove that our methods achieve higher attack success rate and higher transferability than previous gradient-based attack methods.

Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant no. KJQN201901101), and the National Natural Science Foundation of China (Grant no. 61603065).

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Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China

    Wanping Liu & Zhaoping Li

Authors
  1. Wanping Liu
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  2. Zhaoping Li
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Corresponding author

Correspondence to Wanping Liu .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shui Yu

  2. IBM Zurich Research Laboratory, Zurich, Switzerland

    Peter Mueller

  3. Ningbo University, Ningbo, China

    Jiangbo Qian

Appendix A: Details of the Algorithms

Appendix A: Details of the Algorithms

figure b

The algorithm of FI-BI-TI-DIM attack is summarized in Algorithm 2.

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Liu, W., Li, Z. (2020). Enhancing Adversarial Examples with Flip-Invariance and Brightness-Invariance. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_32

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  • DOI: https://doi.org/10.1007/978-981-15-9129-7_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9128-0

  • Online ISBN: 978-981-15-9129-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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