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.
Abstract (translated)
我们介绍了成员推断测试演示器（MINT），以强调更透明的机器学习训练流程的需求。MINT 是一种实验性技术，用于确定某些数据是否在机器学习模型的训练过程中被使用过。我们在流行的面部识别模型上进行了实验，并且使用了包含超过2200万张图像的5个公共数据库。我们实现了令人鼓舞的结果，最高准确率达到89%，这表明有可能判断一个AI模型是否用特定的数据进行过训练。最后，我们推出了一种MINT平台作为这项技术的演示工具，旨在促进人工智能培训过程中的透明度。
URL
https://arxiv.org/abs/2503.08332
