arXiv:2609.27427v1 Announce Type: new Abstract: 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 can be removed for CNNs with both max and average pooling.
Extracting CNNs in the Unknown-Architecture and Feedback-Agnostic Setting
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