Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach

• URL: http://arxiv.org/abs/2511.19316v1
• Date: Mon, 24 Nov 2025 17:11:00 GMT
• Title: Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach
• Authors: Xincheng Wang, Hanchi Sun, Wenjun Sun, Kejun Xue, Wangqiu Zhou, Jianbo Zhang, Wei Sun, Dandan Zhu, Xiongkuo Min, Jun Jia, Zhijun Fang,
• Abstract summary: This paper introduces a general threat model and a comprehensive evaluation framework encompassing Universality, Transmissibility, and Robustness.<n>Experiments show that existing methods perform well in universality and transmissibility, and exhibit some robustness against common image processing operations.<n>The paper further proposes a practical watermark removal method that fully eliminates dataset watermarks without affecting fine-tuning.
• Score: 40.345992095764736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent fine-tuning techniques for diffusion models enable them to reproduce specific image sets, such as particular faces or artistic styles, but also introduce copyright and security risks. Dataset watermarking has been proposed to ensure traceability by embedding imperceptible watermarks into training images, which remain detectable in outputs even after fine-tuning. However, current methods lack a unified evaluation framework. To address this, this paper establishes a general threat model and introduces a comprehensive evaluation framework encompassing Universality, Transmissibility, and Robustness. Experiments show that existing methods perform well in universality and transmissibility, and exhibit some robustness against common image processing operations, yet still fall short under real-world threat scenarios. To reveal these vulnerabilities, the paper further proposes a practical watermark removal method that fully eliminates dataset watermarks without affecting fine-tuning, highlighting a key challenge for future research.

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