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Recent works have demonstrated that machine learning models are vulnerable tomodel inversion attacks, which lead to the exposure of sensitive informationcontained in their training dataset. While some model inversion attacks havebeen developed in the past in the black-box attack setting, in which theadversary does not have direct access to the structure of the model, few ofthese have been conducted so far against complex models such as deep neuralnetworks. In this paper, we introduce GAMIN (for Generative Adversarial ModelINversion), a new black-box model inversion attack framework achievingsignificant results even against deep models such as convolutional neuralnetworks at a reasonable computing cost. GAMIN is based on the continuoustraining of a surrogate model for the target model under attack and a generatorwhose objective is to generate inputs resembling those used to train the targetmodel. The attack was validated against various neural networks used as imageclassifiers. In particular, when attacking models trained on the MNIST dataset,GAMIN is able to extract recognizable digits for up to 60% of labels producedby the target. Attacks against skin classification models trained on the pilotparliament dataset also demonstrated the capacity to extract recognizablefeatures from the targets.

GAMIN: An Adversarial Approach to Black-Box Model Inversion