English(EN) Extraction meets Distillation - cryptanalytic techniques to recover parameters from a Deep Neural Network

新的密码分析方法重建神经网络参数

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一种通过结合密码分析权重方向提取和从预测标签中学习来重建神经网络参数的方法。该方法将ReLU提取技术应用于Leaky ReLU，并包括估计偏置和输出层的方法。实验表明，Leaky ReLU可以在隐藏层中恢复大量神经元，在某些架构中优于ReLU，并且重建的模型在各种指标上与受害者模型高度一致。 AI

影响这项研究可能带来理解和审计深度神经网络的新方法。

排序理由该集群包含一篇详细介绍机器学习新研究方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Biprarshi Biswas, Arya Anshuman Ojha, Yubaraj Das, Devansh Khosla, Kamran Rais, Saankhya Samanta, Subhamoy Maitra, Pantelimon Stanica · 2026-10-27 04:00

提取遇上蒸馏 - 用于从深度神经网络恢复参数的密码分析技术

arXiv:2610.33671v1 Announce Type: cross Abstract: We study fully connected neural-network reconstruction by combining cryptanalytic weight-direction extraction with learning from predicted labels. We adapt a ReLU extraction implementation to Leaky ReLU, compare sign-search method…

本文如何被排名

报道来源 [1]

提取遇上蒸馏 - 用于从深度神经网络恢复参数的密码分析技术

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