新研究探讨了带水印的分布估计的minimax界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了minimax界限来分析带水印对递归离散分布估计的影响。研究表明，在区分合成数据时，水印会降低真实样本的有效性。在真实样本渐近稀缺的情况下，界限表明除非假阴性率也随之降低，否则水印不会带来性能提升。此外，还提出了一种掩蔽程序，以在某些情况下缩小估计器与理论界限之间的差距。 AI

影响为数据识别技术提供了理论见解，可能影响未来在AI应用中区分合成数据与真实数据的方法。

排序理由关于分布估计理论界限的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Millen Kanabar, Michael Gastpar · 2026-09-01 04:00

Minimax界限用于带水印和掩码的递归离散分布估计

arXiv:2608.31091v1 Announce Type: cross Abstract: Watermarking has been proposed as a way to identify synthetic samples in estimation settings where no metadata is available to distinguish them from real samples, but its precise effects remain unexplored. In the absence of a dist…