Auditing the use of data in training machine-learning (ML) models is an increasingly pressing challenge, as myriad ML practitioners routinely leverage the effort of content creators to train models without their permission. In this paper, we propose a general method to audit an ML model for the use of a data-owner's data in training, without prior knowledge of the ML task for which the data might be used. Our method leverages any existing black-box membership inference method, together with a sequential hypothesis test of our own design, to detect data use with a quantifiable, tunable false-detection rate. We show the effectiveness of our proposed framework by applying it to audit data use in two types of ML models, namely image classifiers and foundation models.

翻译：随着众多机器学习从业者经常未经许可地利用内容创作者的成果来训练模型，审计机器学习模型训练中数据的使用正成为日益紧迫的挑战。本文提出一种通用方法，用于审计机器学习模型在训练过程中是否使用了数据所有者的数据，且无需预先了解数据可能被用于何种机器学习任务。该方法结合现有的任意黑盒成员推断方法与我们自行设计的序列假设检验，以可量化、可调节的误检率来检测数据使用情况。我们通过将该框架应用于两类机器学习模型（即图像分类器与基础模型）的数据使用审计，验证了所提框架的有效性。