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Two novel style-transfer palmprint reconstruction attacks

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Abstract

Palmprint has been widely used for personal authentication in many applications, such that the assessment of recognition system security is important. Online attacks of palmprint recognition are much more difficult than offline attacks due to the fewer permissible login and authentication attempts, the unusability of the matching scores, and less training data. A cross-database attack is another challenging problem, where the images reconstructed from a template can still be effective in attacking the systems with other templates. To achieve online cross-database attacks and ensure that the reconstructed images are high-quality, two novel style-transfer methods are proposed to attack coding-based palmprint recognition systems. The two methods are both based on a convolutional neural network, but their optimization objects are different. In the first method, the optimization object is the input image, where a high-quality image can be reconstructed from the binary template. In the second method, the style-transfer neural network is trained with a template dataset and only one style image to reduce the style loss between the source and target domains. The trained style-transfer network can reconstruct approximately 270 images per second. The two methods have highly impressive attack success rates and satisfactorily meet the requirements of the evaluation system.

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Acknowledgments

This work is funded in part by the National Natural Science Foundation of China by the National Natural Science Foundation of China under Grants 61866028, 61866025, and 62162045, the Technology Innovation Guidance Program Project (Special Project of Technology Cooperation, Science and Technology Department of Jiangxi Province) under Grant 20212BDH81003.

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Authors and Affiliations

  1. School of Software, Nanchang Hangkong University, Nanchang, China

    Ziyuan Yang, Lu Leng & Jun Chu

  2. College of Computer Science, Sichuan University, Chengdu, China

    Ziyuan Yang

  3. PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau

    Bob Zhang

  4. School of Information Engineering, Nanchang Hangkong University, Nanchang, China

    Ming Li

Authors
  1. Ziyuan Yang
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  2. Lu Leng
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  3. Bob Zhang
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  4. Ming Li
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  5. Jun Chu
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Contributions

Ziyuan Yang: Writing-Original draft preparation; Conceptualization; Methodology; Validation; Software.

Lu Leng: Methodology, Validation; Supervision; Project administration.

Bob Zhang: Reviewing; Investigation; Formal analysis; Visualization.

Ming Li: Reviewing; Data curation; Resources.

Jun Chu: Reviewing; Editing; Resources.

Corresponding author

Correspondence to Lu Leng.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Yang, Z., Leng, L., Zhang, B. et al. Two novel style-transfer palmprint reconstruction attacks. Appl Intell 53, 6354–6371 (2023). https://doi.org/10.1007/s10489-022-03862-0

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  • DOI: https://doi.org/10.1007/s10489-022-03862-0

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