52 citations · 189 across the 15 of their papers we have counts for
16 papers
Position Matters: Feature Inversion Attacks in ViT Split Inference with Token Reduction and Shuffling
Stefano Leggio, Giulio Rossolini, Alessandro Biondi
Vision Transformers (ViTs) are increasingly used in split-inference systems, where edge devices transmit intermediate token representations to a remote cloud. In this setting, toke…
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…
The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systems
Federico Nesti, Niko Salamini, Mauro Marinoni +4
Recently, the outstanding performance reached by neural networks in many tasks has led to their deployment in autonomous systems, such as robots and vehicles. However, neural netwo…
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…
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…
Edge-Only Universal Adversarial Attacks in Distributed Learning
Giulio Rossolini, Tommaso Baldi, Alessandro Biondi +1
Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems, but may also introdu…
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…
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…
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…
Optimized Partitioning and Priority Assignment of Real-Time Applications on Heterogeneous Platforms with Hardware Acceleration
Daniel Casini, Paolo Pazzaglia, Alessandro Biondi +1
Hardware accelerators, such as those based on GPUs and FPGAs, offer an excellent opportunity to efficiently parallelize functionalities. Recently, modern embedded platforms started…
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…
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…
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…
Increasing the Confidence of Deep Neural Networks by Coverage Analysis
Giulio Rossolini, Alessandro Biondi, Giorgio Buttazzo
The great performance of machine learning algorithms and deep neural networks in several perception and control tasks is pushing the industry to adopt such technologies in safety-c…
Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting
Federico Nesti, Alessandro Biondi, Giorgio Buttazzo
Over the last few years, convolutional neural networks (CNNs) have proved to reach super-human performance in visual recognition tasks. However, CNNs can easily be fooled by advers…