52 citations · 122 across the 21 of their papers we have counts for
11 papers · 1 filter
Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
Giulia Marchiori Pietrosanti, Giulio Rossolini, Giorgio Buttazzo
Vision Transformers (ViTs) remain vulnerable to localized adversarial attacks, e.g., adversarial patches, while recent test-time defenses mitigate them by suppressing image tokens…
PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysis
Andrea Chianese, Giulio Rossolini, Alessandro Biondi +2
Evaluating the performance of visual perception systems for autonomous driving is essential to ensure reliable operation across diverse environmental scenarios. Ideally, a balanced…
Learning Robustness at Test-Time from a Non-Robust Teacher
Stefano Bianchettin, Giulio Rossolini, Giorgio Buttazzo
Nowadays, pretrained models are increasingly used as general-purpose backbones and adapted at test-time to downstream environments where target data are scarce and unlabeled. While…
Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions
Giulia Marchiori Pietrosanti, Giulio Rossolini, Alessandro Biondi +1
The robustness of deep neural networks is a crucial factor in safety-critical applications, particularly in complex and dynamic environments (e.g., medical or driving scenarios) wh…
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…
Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications
Giulio Rossolini, Alessandro Biondi, Giorgio Buttazzo
Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks, achieved through physical objects that can c…
TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation
Gianluca D'Amico, Mauro Marinoni, Federico Nesti +4
The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, whic…
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models
Federico Nesti, Giulio Rossolini, Gianluca D'Amico +2
Adversarial examples represent a serious threat for deep neural networks in several application domains and a huge amount of work has been produced to investigate them and mitigate…
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 Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving
Giulio Rossolini, Federico Nesti, Gianluca D'Amico +3
The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tas…
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks
Federico Nesti, Giulio Rossolini, Saasha Nair +2
Deep learning and convolutional neural networks allow achieving impressive performance in computer vision tasks, such as object detection and semantic segmentation (SS). However, r…