RaMark: Radioactive Watermarking for Generated Tabular Data

• URL: http://arxiv.org/abs/2607.09000v1
• Date: Fri, 10 Jul 2026 00:06:42 GMT
• Title: RaMark: Radioactive Watermarking for Generated Tabular Data
• Abstract summary: RaMark is a radioactive watermarking method that embeds a sinusoidal dependency as an intrinsic component of the data distribution.<n>We show that RaMark achieves substantially stronger radioactivity than seven state-of-the-art methods and consistently outperforms them against both retraining and data modification attacks.
• Score: 26.198071919012033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in generative modeling have made generated tabular data a practical solution for privacy-sensitive data sharing, where watermarking enables ownership verification. However, existing watermarking methods fundamentally fail under retraining attacks, in which an adversary retrains a generative model on a watermarked dataset and regenerates high-utility data that no longer carries the watermark. We address this challenge by introducing radioactivity, the property that a watermark remains detectable after generative model retraining, and propose RaMark, a radioactive watermarking method that embeds a sinusoidal dependency as an intrinsic component of the data distribution. By coupling the watermark with the underlying distribution, RaMark ensures that any generative model preserving data utility also has to preserve the watermark. We theoretically show that with high probability removing watermark degrades utility and alters data distribution. Extensive experiments on two real-world tabular datasets, under a large-scale ownership verification setting with $10^5$ independent data owners, demonstrate that RaMark achieves substantially stronger radioactivity than seven state-of-the-art methods and consistently outperforms them against both retraining and data modification attacks.

Related papers

• Observation-Level Watermarking and Detection for Tabular Data [2.5107166631583215]
  STAMP (Single-observation Tabular Attribution and Marking Procedure) is proposed to accommodate and preserve a wide range of distributions.<n>We also develop a corresponding detection mechanism, which can reliably identify watermarks even when the sample size is as small as one.<n>We demonstrate that the proposed method is effective and robust to subsetting, while maintaining data fidelity and a high detection rate.
  arXiv  Detail & Related papers  (2026-07-12T04:03:55Z)
• Watermarking for Proprietary Dataset Protection [67.79905448913145]
  We argue that output watermarking techniques are the right gadget to make training membership tests for generative models more tractable.<n>We pit a watermark-based dataset inference approach head-to-head against traditional loss-based membership inference methods.
  arXiv  Detail & Related papers  (2026-07-01T01:55:14Z)
• Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs [67.0310240737424]
  We introduce a novel approach to safeguard the ownership of text datasets and effectively detect unauthorized use by the RA-LLMs.<n>Our approach preserves the original data completely unchanged while protecting it by inserting specifically designed canary documents into the IP dataset.<n>During the detection process, unauthorized usage is identified by querying the canary documents and analyzing the responses of RA-LLMs.
  arXiv  Detail & Related papers  (2025-02-15T04:56:45Z)
• Unveiling the Achilles' Heel: Backdoor Watermarking Forgery Attack in Public Dataset Protection [15.597371838613965]
  backdoor watermarks redefine dataset protection as proof of ownership. In this paper, we question the reliability of backdoor watermarks and re-examine them from the perspective of attackers. By exploring the problem of forgery attacks, we reveal the inherent flaws of the dataset ownership verification process.
  arXiv  Detail & Related papers  (2024-11-23T04:39:52Z)
• Data Watermarking for Sequential Recommender Systems [52.207721219147814]
  We explore the problem of data watermarking for sequential recommender systems.<n>We focus on two settings: dataset watermarking, which protects the ownership of the entire dataset, and user watermarking, which safeguards the data of individual users.
  arXiv  Detail & Related papers  (2024-11-20T02:34:21Z)
• TabularMark: Watermarking Tabular Datasets for Machine Learning [20.978995194849297]
  We propose a hypothesis testing-based watermarking scheme, TabularMark. Data noise partitioning is utilized for data perturbation during embedding. Experiments on real-world and synthetic datasets demonstrate the superiority of TabularMark in detectability, non-intrusiveness, and robustness.
  arXiv  Detail & Related papers  (2024-06-21T02:58:45Z)
• Watermarking Generative Tabular Data [39.31042783480766]
  We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity. We also demonstrate appealing robustness against additive noise attack.
  arXiv  Detail & Related papers  (2024-05-22T21:52:12Z)
• TokenMark: A Modality-Agnostic Watermark for Pre-trained Transformers [67.57928750537185]
  TokenMark is a robust, modality-agnostic, robust watermarking system for pre-trained models.<n>It embeds the watermark by fine-tuning the pre-trained model on a set of specifically permuted data samples.<n>It significantly improves the robustness, efficiency, and universality of model watermarking.
  arXiv  Detail & Related papers  (2024-03-09T08:54:52Z)
• ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training [50.77001916246691]
  This paper introduces ClearMark, the first DNN watermarking method designed for intuitive human assessment. ClearMark embeds visible watermarks, enabling human decision-making without rigid value thresholds. It shows an 8,544-bit watermark capacity comparable to the strongest existing work.
  arXiv  Detail & Related papers  (2023-10-25T08:16:55Z)
• A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models [65.40460716619772]
  Our research focuses on the importance of a textbfDistribution-textbfPreserving (DiP) watermark. Contrary to the current strategies, our proposed DiPmark simultaneously preserves the original token distribution during watermarking. It is detectable without access to the language model API and prompts (accessible), and is provably robust to moderate changes of tokens.
  arXiv  Detail & Related papers  (2023-10-11T17:57:35Z)
• Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking [54.40184736491652]
  We propose a backdoor-based watermarking approach that serves as a general framework for safeguarding public-available data. By inserting a small number of watermarking samples into the dataset, our approach enables the learning model to implicitly learn a secret function set by defenders. This hidden function can then be used as a watermark to track down third-party models that use the dataset illegally.
  arXiv  Detail & Related papers  (2023-03-20T21:54:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.