MINT-Demo: Membership Inference Test Demonstrator

• URL: http://arxiv.org/abs/2503.08332v1
• Date: Tue, 11 Mar 2025 11:45:05 GMT
• Title: MINT-Demo: Membership Inference Test Demonstrator
• Authors: Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera, Ruben Tolosana, Ruben Vera-Rodriguez,
• Abstract summary: MINT is a technique for experimentally determining whether certain data has been used during the training of machine learning models.<n>We conduct experiments with popular face recognition models and 5 public databases containing over 22M images.
• Score: 13.795574322456797
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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