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Security Analysis of Alignment-Robust Cancelable Biometric Scheme for Iris Verification

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Information and Communications Security (ICICS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14252))

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Abstract

In cancelable biometric (CB) schemes, secure biometric templates are generated by applying, mainly non-linear, transformations to the origin data. The cancelable templates should satisfy the requirements of irreversibility, unlinkability, and revocability with high accuracy. However, existing cancelable biometric schemes have been demonstrated that their security is overestimated. Many well-known cancelable biometric schemes have been proven vulnerable to some attack models. In this paper, we analyze a recent alignment-robust cancelable biometric scheme called Random Augmented Histogram of Gradients (R\(\cdot \)HoG) that is not as unlinkable as proposed. Moreover, we propose two schemes to attack the unlinkability of R\(\cdot \)HoG. One is that two cancelable templates from different applications are directly connected according to the leaked tokens, and the other is based on the reverse of Z-score transformation, which can achieve higher linkability. Experimental results on CASIA-IrisV3-Interval show that the cancelable biometric template generated by R\(\cdot \)HoG has high linkability with a maximum link success rate of 95.62%.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61806151), and the Natural Science Foundation of Chongqing City (Grant No. CSTC2021JCYJ-MSXMX0002).

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Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, Hubei, China

    Ningjing Fan, Dongdong Zhao & Hucheng Liao

  2. Chongqing Research Institute, Chongqing, China

    Dongdong Zhao

Authors
  1. Ningjing Fan
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  2. Dongdong Zhao
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  3. Hucheng Liao
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Correspondence to Dongdong Zhao .

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Editors and Affiliations

  1. Nankai University, Tianjin, China

    Ding Wang

  2. Columbia University, New York, NY, USA

    Moti Yung

  3. Nankai University, Tianjin, China

    Zheli Liu

  4. Xidian University, Xi’an, China

    Xiaofeng Chen

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Fan, N., Zhao, D., Liao, H. (2023). Security Analysis of Alignment-Robust Cancelable Biometric Scheme for Iris Verification. In: Wang, D., Yung, M., Liu, Z., Chen, X. (eds) Information and Communications Security. ICICS 2023. Lecture Notes in Computer Science, vol 14252. Springer, Singapore. https://doi.org/10.1007/978-981-99-7356-9_16

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  • DOI: https://doi.org/10.1007/978-981-99-7356-9_16

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  • Online ISBN: 978-981-99-7356-9

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