Fingerprint Membership and Identity Inference Against Generative Adversarial Networks
This addresses privacy risks in biometric systems using generative models, but is incremental as it applies known attack methods to a new domain.
The paper tackled the vulnerability of generative models to identity inference by designing an attack on fingerprint datasets generated by a GAN, showing it is effective under various configurations and extendable to other biometrics.
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.