English(EN) Watermarking for Proprietary Dataset Protection

提出LLM训练数据保护的数字水印技术

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-01 01:55

研究人员提出了一种数字水印技术，作为保护用于训练大型语言模型（LLM）的专有数据集的解决方案。该方法旨在通过检测模型输出中残留的数字水印“放射性”来使训练数据成员推断更易于处理。该研究将数字水印与传统的基于损失的成员推断方法进行了比较，发现在子集暴露足够高的情况下，在特定条件下检测性能相当。 AI

影响这项研究可能为保护专有训练数据免遭未经授权的推断提供一种新方法，并可能影响LLM开发中数据集的管理和保护方式。

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arXiv cs.CL TIER_1 English(EN) · John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Tom Goldstein · 2026-07-02 04:00

为专有数据集保护进行水印处理

arXiv:2607.00325v1 Announce Type: cross Abstract: A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output watermarking techniques are the right gadget to make tr…
arXiv cs.CL TIER_1 English(EN) · Tom Goldstein · 2026-07-01 01:55

专有数据集保护的水印技术

A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output watermarking techniques are the right gadget to make training membership tests for generative models more…