ORCID Profile
0000-0003-4975-9408
Current Organisation
UNSW Sydney
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Publisher: IEEE
Date: 08-10-2023
Publisher: Walter de Gruyter GmbH
Date: 12-2021
Abstract: In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning. In autonomous vehicles, various machine learning components are used such as traffic lights recognition, traffic sign recognition, limiting speed and pathfinding. For most of these components, computer vision technologies with deep learning such as object detection, semantic segmentation and image classification are used. However, these machine learning models are vulnerable to targeted tensor perturbations called adversarial attacks, which limit the performance of the applications. Therefore, implementing defense models against adversarial attacks has become an increasingly critical research area. The paper aims at summarising the latest adversarial attacks and defense models introduced in the field of autonomous driving with machine learning technologies up until mid-2021.
Publisher: IEEE
Date: 06-11-2020
Publisher: Asian Journal of Convergence in Technology
Date: 15-12-2020
Publisher: Unpublished
Date: 2022
Publisher: ACM
Date: 17-11-2020
Publisher: IEEE
Date: 23-11-2020
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 07-05-2021
Publisher: The Science and Information Organization
Date: 2021
Publisher: IEEE
Date: 15-12-2020
No related grants have been discovered for K.T.Yasas Mahima.