Giulio Rossolini | Scuola Superiore Sant'Anna

Bio

I'm a third year Ph.D. student of the Scuola Superiore Sant'Anna of Pisa at the Real-Time Systems (ReTiS) Laboratory under the supervision of Prof.Giorgio Buttazzo and Prof. Alessandro Biondi. Previously, I graduated in Embedded Computing Systems, a Master’s Degree offered by the Scuola Superiore Sant’Anna of Pisa and University of Pisa.

Publications

Rossolini, Giulio; Nesti, Federico; D'Amico, Gianluca; Nair, Saasha; Biondi, Alessandro; Buttazzo, Giorgio

On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving Open new browser tab

(2023)
D’Amico, Gianluca; Marinoni, Mauro; Nesti, Federico; Rossolini, Giulio; Buttazzo, Giorgio; Sabina, Salvatore; Lauro, Gianluigi

TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation Open new browser tab

(2023)
Rossolini, Giulio; Nesti, Federico; Brau, Fabio; Biondi, Alessandro; Buttazzo, Giorgio

Defending from Physically-Realizable Adversarial Attacks through Internal Over-Activation Analysis Open new browser tab

(2023)
Brau, F.; Rossolini, G.; Biondi, A.; Buttazzo, G.

Robust-by-Design Classification via Unitary-Gradient Neural Networks Open new browser tab

(2023)
Nesti, Federico; Rossolini, Giulio; Nair, Saasha; Biondi, Alessandro; Buttazzo, Giorgio Carlo

Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks Open new browser tab

(2022)
Rossolini, Giulio; Biondi, Alessandro; Buttazzo, Giorgio

Increasing the Confidence of Deep Neural Networks by Coverage Analysis Open new browser tab

(2022)
Brau, F.; Rossolini, G.; Biondi, A.; Buttazzo, G.

On the Minimal Adversarial Perturbation for Deep Neural Networks With Provable Estimation Error Open new browser tab

(2022)

Bio

Publications

On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving Open new browser tab

TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation Open new browser tab

Defending from Physically-Realizable Adversarial Attacks through Internal Over-Activation Analysis Open new browser tab

Robust-by-Design Classification via Unitary-Gradient Neural Networks Open new browser tab

Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks Open new browser tab

Increasing the Confidence of Deep Neural Networks by Coverage Analysis Open new browser tab

On the Minimal Adversarial Perturbation for Deep Neural Networks With Provable Estimation Error Open new browser tab