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A Protection against the Extraction of Neural Network Models
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Given oracle access to a Neural Network (NN), it is possible to extract its underlying model. We here introduce a protection by adding parasitic layers which keep the underlying NN's predictions mostly unchanged while complexifying the task of reverse-engineering. Our countermeasure relies on approximating a noisy identity mapping with a Convolutional NN. We explain why the introduction of new parasitic layers complexifies the attacks. We report experiments regarding the performance and the accuracy of the protected NN.
Forward citations
Cited by 1 Pith paper
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A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
The paper classifies model extraction attacks and defenses into attack, defense, and computing environment categories and surveys their current state.
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