Welcome to the resource topic for 2025/1970
Title:
Delving into Cryptanalytic Extraction of PReLU Neural Networks
Authors: Yi Chen, Xiaoyang Dong, Ruijie Ma, Yantian Shen, Anyu Wang, Hongbo Yu, Xiaoyun Wang
Abstract:The machine learning problem of model extraction was first
introduced in 1991 and gained prominence as a cryptanalytic challenge
starting with Crypto 2020. For over three decades, research in this field
has primarily focused on ReLU-based neural networks. In this work, we
take the first step towards the cryptanalytic extraction of PReLU neural
networks, which employ more complex nonlinear activation functions
than their ReLU counterparts.
We propose a raw output-based parameter recovery attack for PReLU
networks and extend it to more restrictive scenarios where only the top-m probability scores are accessible. Our attacks are rigorously evaluated
through end-to-end experiments on diverse PReLU neural networks, including models trained on the MNIST dataset. To the best of our knowledge, this is the first practical demonstration of PReLU neural network
extraction across three distinct attack scenarios.
ePrint: https://eprint.iacr.org/2025/1970
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