5 papers
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…
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…
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…
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…