# A General Framework for Data-Use Auditing of ML Models Source: [A General Framework for Data-Use Auditing of ML Models]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:12.945Z Authors: Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter Published: 2024-07-21T09:32:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Dataset Tracking on CIFAR-10](): Dataset Tracking; CIFAR-10; Component Score A 1: 15. - [Dataset Auditing on UCF-101](): Dataset Auditing; UCF-101; Delta-acc: -0.014. - [Dataset Tracking on FairFace](): Dataset Tracking; FairFace; Count A 1 (out of 20): 18. - [Dataset Auditing on HMDB-51](): Dataset Auditing; HMDB-51; Delta-acc: -0.043. ## Original sources and implementation [Original paper]() [PDF]()