15 citations · 19 across the 6 of their papers we have counts for
6 papers
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning
Giulio Rossolini, Fabio Brau, Alessandro Biondi +2
As machine learning models become increasingly deployed across the edge of internet of things environments, a partitioned deep learning paradigm in which models are split across mu…
Video Deblurring by Sharpness Prior Detection and Edge Information
Yang Tian, Fabio Brau, Giulio Rossolini +2
Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Traditional methods directly estimate motion blur kernels, often introduci…
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness
Bernd Prach, Fabio Brau, Giorgio Buttazzo +1
The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical syste…
Robust-by-Design Classification via Unitary-Gradient Neural Networks
Fabio Brau, Giulio Rossolini, Alessandro Biondi +1
The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of…
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis
Giulio Rossolini, Federico Nesti, Fabio Brau +2
This work presents Z-Mask, a robust and effective strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. The pr…
On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Error
Fabio Brau, Giulio Rossolini, Alessandro Biondi +1
Although Deep Neural Networks (DNNs) have shown incredible performance in perceptive and control tasks, several trustworthy issues are still open. One of the most discussed topics…