Membership Inference Test: Auditing Training Data in Object Classification Models

2026-01-19

AI Summary

This work proposes a Membership Inference Test (MINT) framework tailored for object detection models to address the risks of training data memorization and privacy leakage. By analyzing activation patterns in intermediate layers, the method integrates an object detector, an embedding extractor, and a customized MINT module to effectively determine whether a given input sample was part of the training set. Experiments on three public datasets—comprising over 174K images—demonstrate that the proposed approach achieves membership inference accuracy of 70%–80%. The study further identifies key factors influencing inference performance, such as the depth of the detection module’s input layer, thereby significantly enhancing the applicability and auditability of membership inference in complex vision tasks.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Privacy
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this research, we analyze the performance of Membership Inference Tests (MINT), focusing on determining whether given data were utilized during the training phase, specifically in the domain of object recognition. Within the area of object recognition, we propose and develop architectures tailored for MINT models. These architectures aim to optimize performance and efficiency in data utilization, offering a tailored solution to tackle the complexities inherent in the object recognition domain. We conducted experiments involving an object detection model, an embedding extractor, and a MINT module. These experiments were performed in three public databases, totaling over 174K images. The proposed architecture leverages convolutional layers to capture and model the activation patterns present in the data during the training process. Through our analysis, we are able to identify given data used for testing and training, achieving precision rates ranging between 70% and 80%, contingent upon the depth of the detection module layer chosen for input to the MINT module. Additionally, our studies entail an analysis of the factors influencing the MINT Module, delving into the contributing elements behind more transparent training processes.

Problem

• Membership Inference Test
• Training Data Auditing
• Object Recognition
• Data Privacy
• Model Transparency

Innovation

• Membership Inference Test
• Object Recognition
• Convolutional Activation Patterns
• Training Data Auditing
• Model Transparency