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New cryptanalytic method reconstructs neural network parameters

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to reconstruct neural network parameters by combining cryptanalytic weight-direction extraction with learning from predicted labels. This approach adapts ReLU extraction techniques to Leaky ReLU and includes methods for estimating biases and output layers. Experiments show that Leaky ReLU can recover a significant number of neurons in hidden layers, outperforming ReLU in certain architectures, and the reconstructed models achieve high agreement with victim models on various metrics. AI

IMPACT This research could lead to new methods for understanding and potentially auditing deep neural networks.

RANK_REASON The cluster contains an academic paper detailing a new research method in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New cryptanalytic method reconstructs neural network parameters

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The cluster contains an academic paper detailing a new research method in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  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 ·

    Extraction meets Distillation - cryptanalytic techniques to recover parameters from a Deep Neural Network

    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…

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