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An Overview of Laser Injection against Embedded Neural Network Models
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
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
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 features.
Luring of transferable adversarial perturbations in the black-box paradigm
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 inoffensive or make the model more robust against them.
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
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 store neural networks such as weight quantization and more efficient inference methods become major research topics.
