4 papers
Causal Interpretability for Adversarial Robustness: A Hybrid Generative Classification Approach
Chunheng Zhao, Pierluigi Pisu, Gurcan Comert +3
Deep learning-based discriminative classifiers, despite their remarkable success, remain vulnerable to adversarial examples that can mislead model predictions. While adversarial tr…
A Multi-Scale Isolation Forest Approach for Real-Time Detection and Filtering of FGSM Adversarial Attacks in Video Streams of Autonomous Vehicles
Richard Abhulimhen, Negash Begashaw, Gurcan Comert +2
Deep Neural Networks (DNNs) have demonstrated remarkable success across a wide range of tasks, particularly in fields such as image classification. However, DNNs are highly suscept…
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network
Chunheng Zhao, Stefano Longari, Michele Carminati +1
As electronic systems become increasingly complex and prevalent in modern vehicles, securing onboard networks is crucial, particularly as many of these systems are safety-critical.…
Evaluating the Adversarial Robustness of Detection Transformers
Amirhossein Nazeri, Chunheng Zhao, Pierluigi Pisu
Robust object detection is critical for autonomous driving and mobile robotics, where accurate detection of vehicles, pedestrians, and obstacles is essential for ensuring safety. D…