English(EN) OP-Print: Tracing Membership Imprints in On-Policy Self-Distillation

新的OP-Print方法检测AI自蒸馏中的隐私风险

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种名为OP-Print的新方法，用于检测策略内自蒸馏（一种用于AI模型训练的技术）中的隐私风险。该方法专门针对模型在使用自身输出来进行监督时可能记住训练数据的潜在问题。OP-Print在灰盒和黑盒威胁模型下均有效，证明了其在先前攻击失败时的有效性。 AI

影响为评估常见AI训练范式中的隐私漏洞引入了一种新颖的方法。

排序理由详细介绍分析AI模型训练技术新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Qi Li, Xinchao Wang · 2026-10-23 04:00

OP-Print: Tracing Membership Imprints in On-Policy Self-Distillation

arXiv:2610.31616v1 Announce Type: new Abstract: On-policy distillation has been widely adopted for alleviating the distribution mismatch of off-policy distillation. Self-distillation further removes the need for an external teacher by using the student's own outputs, enhanced wit…