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.

本文介绍了一种新的会员推理测试(MINT)方法，旨在通过实证评估人工智能模型的训练过程中是否使用了特定数据。通过两种新的MINT架构，包括基于多层感知器(Multilayer Perceptron)网络和基于卷积神经网络(Convolutional Neural Networks)的架构，本文对具有挑战性的人脸识别任务进行了评估，并对三种最先进的人脸识别模型进行了测试，实验结果表明，我们的MINT方法可以达到高达90%的准确率，提示可以识别出人工智能模型是否使用了特定数据进行训练。

我的数据是否包含在您的AI模型中？用于人脸图像的成员推理测试