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Conference Papers Year : 2022

A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters

Raphaël Joud
  • Function : Author
Jean-Baptiste Rigaud
  • Function : Author

Abstract

Model extraction is a major threat for embedded deep neural network models that leverages an extended attack surface. Indeed, by physically accessing a device, an adversary may exploit side-channel leakages to extract critical information of a model (i.e., its architecture or internal parameters). Different adversarial objectives are possible including a fidelity-based scenario where the architecture and parameters are precisely extracted (model cloning). We focus this work on software implementation of deep neural networks embedded in a high-end 32-bit microcontroller (Cortex-M7) and expose several challenges related to fidelity-based parameters extraction through side-channel analysis, from the basic multiplication operation to the feed-forward connection through the layers. To precisely extract the value of parameters represented in the single-precision floating point IEEE-754 standard, we propose an iterative process that is evaluated with both simulations and traces from a Cortex-M7 target. To our knowledge, this work is the first to target such an high-end 32-bit platform. Importantly, we raise and discuss remaining challenges for the complete extraction of a deep neural network model, more particularly the critical case of biases.

Dates and versions

cea-04038159 , version 1 (20-03-2023)

Identifiers

Cite

Raphaël Joud, Pierre-Alain Moellic, Simon Pontie, Jean-Baptiste Rigaud. A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters. CARDIS 2022, Nov 2022, Birmingham, United Kingdom. pp.45-65, ⟨10.1007/978-3-031-25319-5_3⟩. ⟨cea-04038159⟩
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