PAID: Perturbed Image Attacks Analysis and Intrusion Detection Mechanism for Autonomous Driving Systems
Modern Autonomous Vehicles (AVs) leverage road context information collected through
sensors (eg, LiDAR, radar, and camera) to support the automated driving experience. Once …
sensors (eg, LiDAR, radar, and camera) to support the automated driving experience. Once …
Potential cyber threats of adversarial attacks on autonomous driving models
E Boltachev - Journal of Computer Virology and Hacking Techniques, 2023 - Springer
Abstract Autonomous Vehicles (CAVs) are currently seen as a viable alternative to
traditional vehicles. However, CAVs will face serious cyber threats because many …
traditional vehicles. However, CAVs will face serious cyber threats because many …
Security of camera-based perception for autonomous driving under adversarial attack
Robust perception is crucial for autonomous vehicle security. In this work, we design a
practical adversarial patch attack against camera-based obstacle detection. We identify that …
practical adversarial patch attack against camera-based obstacle detection. We identify that …
Dynamic adversarial attacks on autonomous driving systems
This paper introduces an attacking mechanism to challenge the resilience of autonomous
driving systems. Specifically, we manipulate the decision-making processes of an …
driving systems. Specifically, we manipulate the decision-making processes of an …
Securing Autonomous Vehicles Visual Perception: Adversarial Patch Attack and Defense Schemes With Experimetal Validations
J Liang, R Yi, J Chen, Y Nie… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous vehicles (AVs) heavily depend on machine learning-based algorithms for the
purpose of environmental perception. However, extensively utilized deep learning-based …
purpose of environmental perception. However, extensively utilized deep learning-based …
Adversarial Attacks on Intrusion Detection Systems in In-Vehicle Networks of Connected and Autonomous Vehicles
Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by
breakthroughs in machine learning, yet they encounter significant risks from adversarial …
breakthroughs in machine learning, yet they encounter significant risks from adversarial …
Slowtrack: Increasing the latency of camera-based perception in autonomous driving using adversarial examples
In Autonomous Driving (AD), real-time perception is a critical component responsible for
detecting surrounding objects to ensure safe driving. While researchers have extensively …
detecting surrounding objects to ensure safe driving. While researchers have extensively …
Adversarial attack of ml-based intrusion detection system on in-vehicle system using gan
In recent years, research has focused on developing intrusion detection systems (IDS) within
vehicle networks to prevent automotive hacking from external cyberattacks. While machine …
vehicle networks to prevent automotive hacking from external cyberattacks. While machine …
Exploration of machine learning attacks in automotive systems using physical and mixed reality platforms
Adversarial attacks on Deep Neural Networks represent a critical challenge in the adoption
of DNNs in critical applications. However,-and in spite of its great need,-there is significant …
of DNNs in critical applications. However,-and in spite of its great need,-there is significant …
Detecting stealthy cyberattacks on automated vehicles via generative adversarial networks
The emergence of vehicles with driver-assist features, including adaptive cruise control
(ACC) or other automated driving capabilities, introduces the possibility of cyberattacks …
(ACC) or other automated driving capabilities, introduces the possibility of cyberattacks …