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.

翻译：生成模型作为潜在的新型工业革命催化剂正受到广泛关注。由于自动样本生成有助于解决通常影响已训练生物特征模型的隐私和数据稀缺问题，此类技术在该领域得到了广泛应用。本文通过设计并测试针对生成对抗网络创建的指纹数据集的身份推断攻击，评估了生成式机器学习模型在身份保护方面的脆弱性。实验结果表明，所提出的解决方案在不同配置下均被证明有效，且易于扩展至其他生物特征度量。