Membership Inference Test: Auditing Training Data in Object Classification Models
This work addresses the need for more transparent training processes in object recognition, though it appears incremental as it focuses on optimizing existing MINT methods for this specific domain.
The research tackled the problem of auditing training data in object classification models using Membership Inference Tests (MINT), achieving precision rates of 70-80% in identifying whether data were used during training across experiments with over 174K images.
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.