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arXiv · 2503.08332

MINT-Demo: Membership Inference Test Demonstrator

Daniel DeAlcala·Aythami Morales·Julian Fierrez·Gonzalo Mancera·Ruben Tolosana·Ruben Vera-Rodriguez
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Abstract

We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determining whether certain data has been used during the training of machine learning models. We conduct experiments with popular face recognition models and 5 public databases containing over 22M images. Promising results, up to 89% accuracy are achieved, suggesting that it is possible to recognize if an AI model has been trained with specific data. Finally, we present a MINT platform as demonstrator of this technology aimed to promote transparency in AI training.

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Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera, Ruben Tolosana, Ruben Vera-Rodriguez. 2025-03-11. MINT-Demo: Membership Inference Test Demonstrator. https://arxiv.org/abs/2503.08332

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