arXiv Machine Learning July 13, 2026 By Xin Che, Lingyang Chu, Qiqi Zhang, Xinyu Ma, Xuan Luo, Jian Pei

RaMark: Radioactive Watermarking for Generated Tabular Data

Read the original on arXiv Machine Learning →

arXiv:2607. 09000v1 Announce Type: cross Abstract: Recent advances in generative modeling have made generated tabular data a practical solution for privacy-sensitive data sharing, where watermarking enables ownership verification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 5

Attribute-based Undetectable Watermarking for Generative AI Models

arXiv:2608. 03174v1 Announce Type: cross Abstract: Generative AI systems increasingly produce content whose provenance is difficult to verify, motivating watermarking techniques for identifying model-generated outputs.

By Miryam Mi-Ying Huang, Chung-Wei Lee, Max Raffel, Er-Cheng Tang

safety

arXiv AI

Jun 11

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

arXiv:2606. 11698v1 Announce Type: cross Abstract: Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures.

By Jian-Ping Mei, Weibin Zhang, Ao Yao, Tiantian Zhu, Jie Xiao

rag fine-tuning

arXiv Machine Learning

Jul 14

Observation-Level Watermarking and Detection for Tabular Data

arXiv:2607. 10554v1 Announce Type: cross Abstract: With the development of generative AI, watermarking techniques have been widely used to detect the authenticity of AI-generated data and protect the rights of users and creators.

By Dongyu Cui, Xuan Bi

arXiv Machine Learning

Jul 2

Watermarking for Proprietary Dataset Protection

arXiv:2607. 00325v1 Announce Type: new Abstract: A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings.

By John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Tom Goldstein

llms

arXiv AI

Aug 21

A Locally Tokenized Generative Model for Robust Time-Series Watermarking

arXiv:2608. 19727v1 Announce Type: cross Abstract: Watermarking is a central tool for provenance in generative models, yet its application to multivariate time series remains hindered by reliability failures under post-editing attacks.

By Dongbin Kim, Geonwoo Shin, Yujin Choi, Soyeon Park, Jaewook Lee

benchmarks safety

arXiv Computer Vision

3d ago

Persistent Watermarking of Text-to-Image Models

arXiv:2609.39024v1 Announce Type: new Abstract: Text-to-image (T2I) generation is gaining increasing popularity with the general public, motivating the development of reliable mechanisms for copyrigh...

By Dixi Yao, Kaiwen Chen, Tahseen Rabbani, Tian Li

diffusion fine-tuning

One email a morning, machine-written

Related stories

Attribute-based Undetectable Watermarking for Generative AI Models

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

Observation-Level Watermarking and Detection for Tabular Data

Watermarking for Proprietary Dataset Protection

A Locally Tokenized Generative Model for Robust Time-Series Watermarking

Persistent Watermarking of Text-to-Image Models