# Dataset Ownership Verification in Contrastive Pre-trained Models

arXiv: https://arxiv.org/abs/2502.07276 · HTML: https://mlsanity.com/paper/2502.07276/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?](https://mlsanity.com/paper/2503.09122/) (2025)
- [Dataset Ownership Verification for Pre-trained Masked Models](https://mlsanity.com/paper/2507.12022/) (2025)
- [PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking](https://mlsanity.com/paper/2505.12296/) (2025)
- [ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders](https://mlsanity.com/paper/2604.22550/) (2026)
- [Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning](https://mlsanity.com/paper/2605.01834/) (2026)
- [Trustworthy synthetic visual media: Evidence across the media lifecycle](https://mlsanity.com/paper/2609.34232/) (2026)

## Abstract (arXiv)

High-quality open-source datasets, which necessitate substantial efforts for curation, has become the primary catalyst for the swift progress of deep learning. Concurrently, protecting these datasets is paramount for the well-being of the data owner. Dataset ownership verification emerges as a crucial method in this domain, but existing approaches are often limited to supervised models and cannot be directly extended to increasingly popular unsupervised pre-trained models. In this work, we propose the first dataset ownership verification method tailored specifically for self-supervised pre-trained models by contrastive learning. Its primary objective is to ascertain whether a suspicious black-box backbone has been pre-trained on a specific unlabeled dataset, aiding dataset owners in upholding their rights. The proposed approach is motivated by our empirical insights that when models are trained with the target dataset, the unary and binary instance relationships within the embedding space exhibit significant variations compared to models trained without the target dataset. We validate the efficacy of this approach across multiple contrastive pre-trained models including SimCLR, BYOL, SimSiam, MOCO v3, and DINO. The results demonstrate that our method rejects the null hypothesis with a p-value markedly below 0.05, surpassing all previous methodologies. Our code is available at https://github.com/xieyc99/DOV4CL.