52 citations · 65 across the 3 of their papers we have counts for
3 papers
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…
Falsification-Based Robust Adversarial Reinforcement Learning
Xiao Wang, Saasha Nair, Matthias Althoff
Reinforcement learning (RL) has achieved enormous progress in solving various sequential decision-making problems, such as control tasks in robotics. Since policies are overfitted…