18 citations · 18 across the 10 of their papers we have counts for
11 papers
Bringing Chip Tapeout Into University Education
Luca Pezzarossa, Martin Schoeberl, Matti Käyrä +5
Providing students with experience from chip specification to fabricated silicon can strengthen chip-design education, but integrating tapeout into regular teaching is difficult to…
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…
Watch Out for the Lifespan: Evaluating Backdoor Attacks Against Federated Model Adaptation
Bastien Vuillod, Pierre-Alain Moellic, Jean-Max Dutertre
Large models adaptation through Federated Learning (FL) addresses a wide range of use cases and is enabled by Parameter-Efficient Fine-Tuning techniques such as Low-Rank Adaptation…
Fault Injection on Embedded Neural Networks: Impact of a Single Instruction Skip
Clement Gaine, Pierre-Alain Moellic, Olivier Potin +1
With the large-scale integration and use of neural network models, especially in critical embedded systems, their security assessment to guarantee their reliability is becoming an…
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 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…
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.…
SideLine: How Delay-Lines (May) Leak Secrets from your SoC
Joseph Gravellier, Jean-Max Dutertre, Yannick Teglia +1
To meet the ever-growing need for performance in silicon devices, SoC providers have been increasingly relying on software-hardware cooperation. By controlling hardware resources s…
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…