… with a high error rate, so as to manipulate the vehicle’s behavior. This paper will focus on
adversarial attacks targeting the camera sensor. Methods for generating adversaries can be …

… of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks
in … 1) Adversarial Attacks: An adversarial attack affecting the training phase of the learning …

… autonomous vehicle (AV)-based applications is constrained by the DL models’ susceptibility
to adversarial attacks … defense models into practice against adversarial attacks has grown in …

… The main objective of this research is to design black-box adversarial attacks for AV for
exposing vulnerabilities in deep learning models. We propose a “multi-gradient” attack in deep …

… of a strong attack, the adversarial properties would not hold … correctly classified in the
context of an autonomous vehicle. … This adversarial attack against the detector may seem a bit …

… safety of autonomous vehicles under adversarial attacks. In this … of adversarial attacks,
perturbation attacks, and patch attacks, on the driving safety of vision-based autonomous vehicles …

… learning models to adversarial samples makes the autonomous vehicles suffer severe …
adversarial samples, only a few of them are designated for the scenario of autonomous vehicles…

… system to check the vehicle state [40], [48]. First, we monitor model prediction latency caused
by adversarial attacks. Second, since autonomous vehicles are resource constrained, we …

… Such instances clearly demonstrate that the Machine Learning is just another tool, susceptible
to adversarial attacks which can have huge implications in a world where we trust them …

… -based adversarial attack against urban scene segmentation for autonomous vehicles.
Notably, in the above works on attacking scene segmentation, the DeepLab [13] framework …