Is my Data in your AI Model? Membership Inference Test with Application
  to Face Images
Daniel DeAlcala Aythami Morales Gonzalo Mancera Julian Fierrez Ruben Tolosana Javier Ortega-Garcia
This paper introduces the Membership Inference Test (MINT), a novel approach
that aims to empirically assess if specific data was used during the training
of Artificial Intelligence (AI) models. Specifically, we propose two novel MINT
architectures designed to learn the distinct activation patterns that emerge
when an audited model is exposed to data used during its training process. The
first architecture is based on a Multilayer Perceptron (MLP) network and the
second one is based on Convolutional Neural Networks (CNNs). The proposed MINT
architectures are evaluated on a challenging face recognition task, considering
three state-of-the-art face recognition models. Experiments are carried out
using six publicly available databases, comprising over 22 million face images
in total. Also, different experimental scenarios are considered depending on
the context available of the AI model to test. Promising results, up to 90%
accuracy, are achieved using our proposed MINT approach, suggesting that it is
possible to recognize if an AI model has been trained with specific data.