Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866918156495224832 |
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| author | DeAlcala, Daniel Morales, Aythami Fierrez, Julian Mancera, Gonzalo Tolosana, Ruben Ortega-Garcia, Javier |
| author_facet | DeAlcala, Daniel Morales, Aythami Fierrez, Julian Mancera, Gonzalo Tolosana, Ruben Ortega-Garcia, Javier |
| contents | This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to learn the distinct activation patterns that emerge when an Audited Model is exposed to data used during its training process. These architectures are based on Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The experimental framework focuses on the challenging task of Face Recognition, considering three state-of-the-art Face Recognition systems. Experiments are carried out using six publicly available databases, comprising over 22 million face images in total. Different experimental scenarios are considered depending on the context of the AI model to test. Our proposed MINT approach achieves promising results, with up to 90\% accuracy, indicating the potential to recognize if an AI model has been trained with specific data. The proposed MINT approach can serve to enforce privacy and fairness in several AI applications, e.g., revealing if sensitive or private data was used for training or tuning Large Language Models (LLMs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09225 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics DeAlcala, Daniel Morales, Aythami Fierrez, Julian Mancera, Gonzalo Tolosana, Ruben Ortega-Garcia, Javier Computer Vision and Pattern Recognition Artificial Intelligence This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to learn the distinct activation patterns that emerge when an Audited Model is exposed to data used during its training process. These architectures are based on Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The experimental framework focuses on the challenging task of Face Recognition, considering three state-of-the-art Face Recognition systems. Experiments are carried out using six publicly available databases, comprising over 22 million face images in total. Different experimental scenarios are considered depending on the context of the AI model to test. Our proposed MINT approach achieves promising results, with up to 90\% accuracy, indicating the potential to recognize if an AI model has been trained with specific data. The proposed MINT approach can serve to enforce privacy and fairness in several AI applications, e.g., revealing if sensitive or private data was used for training or tuning Large Language Models (LLMs). |
| title | Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2402.09225 |