RaMark: Radioactive Watermarking for Generated Tabular Data
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 c...
| Authors | Xin Che, Lingyang Chu, Qiqi Zhang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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