A Study on Adversarial Fingerprint Defense Using Self-Supervised Denoising from Single Image 


Vol. 48,  No. 7, pp. 833-841, Jul.  2023
10.7840/kics.2023.48.7.833


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  Abstract

Adversarial fingerprint attacks exploit deep learning-based fingerprint authentication systems, causing abnormal behavior in the model. The emergence of various forms of adversarial attacks has created vulnerabilities in deep learning-based fingerprint authentication systems, leading to a new security issue. In this paper, we propose a defense mechanism that provides generalized performance against various adversarial fingerprint attacks without requiring multiple types of fingerprint images. The proposed method utilizes a single image-based self-supervised denoising technique to effectively remove adversarial noise from input fingerprint images while restoring them to their original state, providing robust defense performance against adversarial fingerprint attacks. Furthermore, it offers superior defense performance compared to existing image restoration methods without requiring pre-training on large-scale fingerprint images.

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  Cite this article

[IEEE Style]

P. M. Hong, H. Yoo, T. Kim, J. W. Yoon, T. H. Kim, Y. K. Lee, "A Study on Adversarial Fingerprint Defense Using Self-Supervised Denoising from Single Image," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 7, pp. 833-841, 2023. DOI: 10.7840/kics.2023.48.7.833.

[ACM Style]

Pyo Min Hong, Hwajung Yoo, Taeyong Kim, Jung Won Yoon, Tae Hyung Kim, and Youn Kyu Lee. 2023. A Study on Adversarial Fingerprint Defense Using Self-Supervised Denoising from Single Image. The Journal of Korean Institute of Communications and Information Sciences, 48, 7, (2023), 833-841. DOI: 10.7840/kics.2023.48.7.833.

[KICS Style]

Pyo Min Hong, Hwajung Yoo, Taeyong Kim, Jung Won Yoon, Tae Hyung Kim, Youn Kyu Lee, "A Study on Adversarial Fingerprint Defense Using Self-Supervised Denoising from Single Image," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 7, pp. 833-841, 7. 2023. (https://doi.org/10.7840/kics.2023.48.7.833)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880