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.

翻译：本研究分析了成员推断测试（MINT）的性能，重点在于判定给定数据是否在训练阶段被使用，特别是在目标识别领域。在目标识别范围内，我们提出并开发了专为MINT模型定制的架构。这些架构旨在优化数据利用的性能与效率，为解决目标识别领域固有的复杂性提供定制化解决方案。我们开展了包含目标检测模型、嵌入提取器和MINT模块的实验。这些实验在三个公共数据库中进行，总计超过17.4万张图像。所提出的架构利用卷积层来捕获并建模训练过程中数据中存在的激活模式。通过分析，我们能够识别用于测试和训练的给定数据，其准确率在70%至80%之间，具体取决于输入MINT模块的检测模块层深度。此外，我们的研究还包含对影响MINT模块因素的分析，深入探讨了实现更透明训练过程背后的关键要素。