  Model extraction emerges as a critical security threat with attack vectors
exploiting both algorithmic and implementation-based approaches. The main goal
of an attacker is to steal as much information as possible about a protected
victim model, so that he can mimic it with a substitute model, even with a
limited access to similar training data. Recently, physical attacks such as
fault injection have shown worrying efficiency against the integrity and
confidentiality of embedded models. We focus on embedded deep neural network
models on 32-bit microcontrollers, a widespread family of hardware platforms in
IoT, and the use of a standard fault injection strategy - Safe Error Attack
(SEA) - to perform a model extraction attack with an adversary having a limited
access to training data. Since the attack strongly depends on the input
queries, we propose a black-box approach to craft a successful attack set. For
a classical convolutional neural network, we successfully recover at least 90%
of the most significant bits with about 1500 crafted inputs. These information
enable to efficiently train a substitute model, with only 8% of the training
dataset, that reaches high fidelity and near identical accuracy level than the
victim model.

Cryptography and Security;Artificial Intelligence