Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

Understand

As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject.

• Even those that deploy neural networks embedded in physical devices may wish to keep the inner working of their designs hidden -- either to protect their intellectual property or as a form of protection from adversarial inputs.
• The specific problem we address is how, through heavy system stack, given noisy and imperfect memory traces, one might reconstruct the neural network architecture including the set of layers employed, their connectivity, and their respective dimension sizes.
• Considering both the intra-layer architecture features and the inter-layer temporal association information introduced by the DNN design empirical experience, we draw upon ideas from speech recognition to solve this problem.