1 citations · 2 across the 10 of their papers we have counts for
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Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips
Bastien Vuillod, Kevin Hector, Pierre-Alain Moellic +2
Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized…
Like an Open Book? Read Neural Network Architecture with Simple Power Analysis on 32-bit Microcontrollers
Raphael Joud, Pierre-Alain Moellic, Simon Pontie +1
Model extraction is a growing concern for the security of AI systems. For deep neural network models, the architecture is the most important information an adversary aims to recove…
Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models
Kevin Hector, Pierre-Alain Moellic, Mathieu Dumont +1
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…
Evaluation of Parameter-based Attacks against Embedded Neural Networks with Laser Injection
Mathieu Dumont, Kevin Hector, Pierre-Alain Moellic +2
Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of…
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters
Raphael Joud, Pierre-Alain Moellic, Simon Pontie +1
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 exp…
A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks
Kevin Hector, Mathieu Dumont, Pierre-Alain Moellic +1
Deep neural network models are massively deployed on a wide variety of hardware platforms. This results in the appearance of new attack vectors that significantly extend the standa…