Polynomial Time Cryptanalytic Extraction of Neural Network Models

ePrint Report: Polynomial Time Cryptanalytic Extraction of Neural Network Models

Adi Shamir, Isaac Canales-Martinez, Anna Hambitzer, Jorge Chavez-Saab, Francisco Rodrigez-Henriquez, Nitin Satpute

Billions of dollars and countless GPU hours are currently
spent on training Deep Neural Networks (DNNs) for a variety of tasks.
Thus, it is essential to determine the difficulty of extracting all the parameters of such neural networks when given access to their black-box
implementations. Many versions of this problem have been studied over
the last 30 years, …

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