# Robust Spectral Watermark for Synthetic Tabular Data

arXiv: https://arxiv.org/abs/2511.21600 · HTML: https://mlsanity.com/paper/2511.21600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 16 of 16)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [On Provable Copyright Protection for Generative Models](https://mlsanity.com/paper/2302.10870/) (2023)
- [A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules](https://mlsanity.com/paper/2404.01245/) (2024)
- [A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models](https://mlsanity.com/paper/2310.07710/) (2023)
- [TabMT: Generating tabular data with masked transformers](https://mlsanity.com/paper/2312.06089/) (2023)
- [Attack-Resilient Image Watermarking Using Stable Diffusion](https://mlsanity.com/paper/2401.04247/) (2024)
- [Robust Detection of Watermarks for Large Language Models Under Human Edits](https://mlsanity.com/paper/2411.13868/) (2024)
- [Optimizing watermarks for large language models](https://mlsanity.com/paper/2312.17295/) (2023)
- [Debiasing Watermarks for Large Language Models via Maximal Coupling](https://mlsanity.com/paper/2411.11203/) (2024)
- [TabularMark: Watermarking Tabular Datasets for Machine Learning](https://mlsanity.com/paper/2406.14841/) (2024)

## Built on by (most cited first, 1 of 1)

- [Observation-Level Watermarking and Detection for Tabular Data](https://mlsanity.com/paper/2607.10554/) (2026)

## Abstract (arXiv)

The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance and misuse. Watermarking offers a promising solution to address these concerns by ensuring the traceability of synthetic data, but existing methods face many limitations: they are computationally expensive due to reliance on the inverse process of large diffusion models, struggle with mixed discrete-continuous data, or lack robustness to common post-processing attacks. To address these limitations, we propose TAB-DRW, an efficient and robust post-editing watermarking scheme for synthetic tabular data. TAB-DRW embeds watermark signals in the frequency domain: it normalizes heterogeneous features via the Yeo-Johnson transformation and standardization, applies the discrete Fourier transform (DFT), and adjusts the imaginary parts of adaptively selected entries according to precomputed pseudorandom bits. To further enhance robustness and efficiency, we introduce a novel rank-based pseudorandom bit generation method that enables row-wise retrieval without incurring storage overhead. Experiments on five benchmark tabular datasets show that TAB-DRW achieves strong detectability and robustness against post-processing and adaptive attacks, while preserving high data fidelity and fully supporting mixed-type features.