An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network

Amit Daniely, Elad Granot

Keywords: Learning With Queries, ReLU Networks, Model Extraction

TL;DR: A first polynomial-time algorithm to extract the parameters and architecture of two- and three-layer neural networks using membership-queries

Abstract:

We consider the natural problem of learning a ReLU network from queries, which was recently remotivated by model extraction attacks. In this work, we present a polynomial-time algorithm that can learn a depth-two ReLU network from queries under mild general position assumptions. We also present a polynomial-time algorithm that, under mild general position assumptions, can learn a rich class of depth-three ReLU networks from queries. For instance, it can learn most networks where the number of first layer neurons is smaller than the dimension and the number of second layer neurons.

These two results substantially improve state-of-the-art: Until our work, polynomial-time algorithms were only shown to learn from queries depth-two networks under the assumption that either the underlying distribution is Gaussian (Chen et al. (2021)) or that the weights matrix rows are linearly independent (Milli et al. (2019)). For depth three or more, there were no known poly-time results.

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