Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review

Applied Computer Systems

Poravi Guhanathan¹  Mahima K. T. Y.¹  Ayoob Mohamed¹ 

[1] Department of Software Engineering, Informatics Institute of Technology, Colombo, Sri Lanka;

关键词: adversarial attacks;    adversarial robustness;    autonomous driving;    computer vision;    machine learning;   

DOI  :  10.2478/acss-2021-0012

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown