A Protection against the Extraction of Neural Network Models

Understand

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.