1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
Luring of transferable adversarial perturbations in the black-box paradigm
Rémi Bernhard, Pierre-Alain Moellic, Jean-Max Dutertre
The growing interest for adversarial examples, i.e. maliciously modified examples which fool a classifier, has resulted in many defenses intended to detect them, render them inoffe…
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
Rémi Bernhard, Pierre-Alain Moellic, Jean-Max Dutertre
As the will to deploy neural networks models on embedded systems grows, and considering the related memory footprint and energy consumption issues, finding lighter solutions to sto…