# DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration Source: [DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration]() Collection index: [llms.txt]() Data updated: 2026-06-24T05:00:04.551Z Authors: Tian Dong, Yan Meng, Shaofeng Li, Guoxing Chen, Yuling Chen, Zhen Liu, Haojin Zhu, Hao Chen Published: 2026-06-19T09:08:43.000Z ## Abstract Training datasets have tremendous proprietary value and are vulnerable to unauthorized copying. Existing defenses mainly focus on tracking individual data points, but pay little attention to the threat of dataset regeneration. Through a measurement study of public tumor datasets, we identify substantial real-world partial-dataset replication, raising concerns about potential license noncompliance. To counter the challenge of tracking previously unknown adversarial regeneration, our key insight is that regeneration that preserves model utility inevitably preserves measurable signals across multiple feature scales. We categorize these dataset features into sample-, set-, and distribution-level features and design four similarity metrics to accurately identify regeneration. Based on these metrics, we develop DIPBox, which to our knowledge is the first testing framework that tracks regeneration suspects via multi-scale similarity testing across a spectrum of defender access settings, from limited to full information. We further provide a learning-theoretic analysis that justifies these multi-scale metrics and formalizes an inherent utility--divergence trade-off, implying fundamental limits on evasive regeneration. Extensive experiments on 16 vision and text base datasets, 320 regenerated datasets, and 590 derived models validate that DIPBox outperforms previous solutions while characterizing its robustness and limits under three adaptive attacks. ## 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: 20. - [Dataset Tracking on FairFace](): Dataset Tracking; FairFace; Count A 1 (out of 20): 20. ## Original sources and implementation [Original paper]() [PDF]()