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Extracting CNNs in the Unknown-Architecture and Feedback-Agnostic Setting
This paper studies the cryptanalytic extraction of convolutional neural networks (CNNs). Existing cryptanalytic extraction attacks on CNNs assume that the network architecture is known, and try to recover model parameters.In this paper, we prove for the first time that the architecture assumption ca
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