Recent advances in machine learning, especially techniques such as deep neural networks,
are promoting a range of high-stakes applications, including autonomous driving, which …

Abstract Machine learning models are known to be susceptible to adversarial perturbation.
One famous attack is the adversarial patch, a particularly crafted sticker that makes the …

With deep neural networks (DNNs) involved in more and more decision making processes,
critical security problems can occur when DNNs give wrong predictions. This can be …

The attacks on the neural-network-based classifiers using adversarial images have gained a
lot of attention recently. An adversary can purposely generate an image that is …

The existence of real-world adversarial examples (RWAEs)(commonly in the form of
patches) poses a serious threat for the use of deep learning models in safety-critical …

Although deep neural networks (DNNs) are high-performance methods for various complex
tasks, eg, environment perception in automated vehicles (AVs), they are vulnerable to …

The perception system is a safety-critical component that directly impacts the overall safety
of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep …

Deep learning model has been widely studied and proven to achieve high accuracy in
various pattern recognition tasks, especially in image recognition. However, due to its non …

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 …

Deep neural networks (DNNs) are being incorporated into various autonomous systems like
self-driving cars and robots. However, there is a rising concern about the robustness of …