Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data

9 Dec 2019Itay MosafiEli DavidNathan S. Netanyahu

As state-of-the-art deep neural networks are deployed at the core of more advanced Al-based products and services, the incentive for copying them (i.e., their intellectual properties) by rival adversaries is expected to increase considerably over time. The best way to extract or steal knowledge from such networks is by querying them using a large dataset of random samples and recording their output, followed by training a student network to mimic these outputs, without making any assumption about the original networks... (read more)

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