An analysis of adversarial attacks and defenses on autonomous driving models
Nowadays, autonomous driving has attracted much attention from both industry and
academia. Convolutional neural network (CNN) is a key component in autonomous driving …
academia. Convolutional neural network (CNN) is a key component in autonomous driving …
Simple physical adversarial examples against end-to-end autonomous driving models
Recent advances in machine learning, especially techniques such as deep neural networks,
are promoting a range of high-stakes applications, including autonomous driving, which …
are promoting a range of high-stakes applications, including autonomous driving, which …
Cybersecurity of autonomous vehicles: A systematic literature review of adversarial attacks and defense models
Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …
development and improvement of deep learning (DL) technology. However, the uptake of …
Does physical adversarial example really matter to autonomous driving? towards system-level effect of adversarial object evasion attack
In autonomous driving (AD), accurate perception is indispensable to achieving safe and
secure driving. Due to its safety-criticality, the security of AD perception has been widely …
secure driving. Due to its safety-criticality, the security of AD perception has been widely …
Evaluating adversarial attacks on driving safety in vision-based autonomous vehicles
In recent years, many deep learning models have been adopted in autonomous driving. At
the same time, these models introduce new vulnerabilities that may compromise the safety …
the same time, these models introduce new vulnerabilities that may compromise the safety …
Are self-driving cars secure? evasion attacks against deep neural networks for steering angle prediction
Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-
driving cars. However, the security of DNN models in this context leads to major safety …
driving cars. However, the security of DNN models in this context leads to major safety …
Sok: Certified robustness for deep neural networks
Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …
Review of artificial intelligence adversarial attack and defense technologies
S Qiu, Q Liu, S Zhou, C Wu - Applied Sciences, 2019 - mdpi.com
In recent years, artificial intelligence technologies have been widely used in computer
vision, natural language processing, automatic driving, and other fields. However, artificial …
vision, natural language processing, automatic driving, and other fields. However, artificial …
Mitigating evasion attacks to deep neural networks via region-based classification
Deep neural networks (DNNs) have transformed several artificial intelligence research
areas including computer vision, speech recognition, and natural language processing …
areas including computer vision, speech recognition, and natural language processing …
Attacks on machine learning: Adversarial examples in connected and autonomous vehicles
P Sharma, D Austin, H Liu - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Connected and autonomous vehicles (CAV aka driverless cars) offset human response for
transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety …
transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety …