Fingerprint Membership and Identity Inference Against Generative Adversarial Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929394488967168 |
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| author | Cavasin, Saverio Mari, Daniele Milani, Simone Conti, Mauro |
| author_facet | Cavasin, Saverio Mari, Daniele Milani, Simone Conti, Mauro |
| contents | Generative models are gaining significant attention as potential catalysts for a novel industrial revolution. Since automated sample generation can be useful to solve privacy and data scarcity issues that usually affect learned biometric models, such technologies became widely spread in this field. In this paper, we assess the vulnerabilities of generative machine learning models concerning identity protection by designing and testing an identity inference attack on fingerprint datasets created by means of a generative adversarial network. Experimental results show that the proposed solution proves to be effective under different configurations and easily extendable to other biometric measurements. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_15253 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fingerprint Membership and Identity Inference Against Generative Adversarial Networks Cavasin, Saverio Mari, Daniele Milani, Simone Conti, Mauro Computer Vision and Pattern Recognition I.4.9; I.5.4 Generative models are gaining significant attention as potential catalysts for a novel industrial revolution. Since automated sample generation can be useful to solve privacy and data scarcity issues that usually affect learned biometric models, such technologies became widely spread in this field. In this paper, we assess the vulnerabilities of generative machine learning models concerning identity protection by designing and testing an identity inference attack on fingerprint datasets created by means of a generative adversarial network. Experimental results show that the proposed solution proves to be effective under different configurations and easily extendable to other biometric measurements. |
| title | Fingerprint Membership and Identity Inference Against Generative Adversarial Networks |
| topic | Computer Vision and Pattern Recognition I.4.9; I.5.4 |
| url | https://arxiv.org/abs/2406.15253 |