MINT-Demo: Membership Inference Test Demonstrator

2025-03-11

AI Summary

To address insufficient transparency in machine learning training data, this paper proposes and implements the first publicly demonstrable membership inference testing platform, enabling empirical determination of whether a specific sample was included in model training. Methodologically, we design a membership inference framework grounded in statistical significance testing and black-box model behavior analysis, systematically validated on a facial image dataset exceeding 22 million samples. By integrating heterogeneous facial data sources and mainstream recognition models, our framework supports cross-model generalization evaluation. Experiments achieve up to 89% membership identification accuracy across multiple publicly available face recognition models. This work represents the first engineering realization of membership inference as a reproducible, auditable open platform—establishing a novel paradigm for traceability and regulatory compliance verification in AI training processes.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Adversarial Attacks & Robustness
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Develops a technique to detect if specific data was used in AI model training.
• Tests the technique on face recognition models using large public image databases.
• Promotes transparency in AI training processes through a demonstrative platform.

Innovation

• MINT technique detects training data usage
• Experiments with face recognition models
• MINT platform promotes AI training transparency