5 papers
An Overview of Laser Injection against Embedded Neural Network Models
Mathieu Dumont, Pierre-Alain Moellic, Raphael Viera +2
For many IoT domains, Machine Learning and more particularly Deep Learning brings very efficient solutions to handle complex data and perform challenging and mostly critical tasks.…
A Review of Confidentiality Threats Against Embedded Neural Network Models
Raphaël Joud, Pierre-Alain Moellic, Rémi Bernhard +1
Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN) models, becomes a widely accepted standard in many domains more particularly IoT-based systems…
Impact of Spatial Frequency Based Constraints on Adversarial Robustness
Rémi Bernhard, Pierre-Alain Moellic, Martial Mermillod +4
Adversarial examples mainly exploit changes to input pixels to which humans are not sensitive to, and arise from the fact that models make decisions based on uninterpretable featur…
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…