Traffic sign recognition is an essential component of perception in autonomous vehicles,
which is currently performed almost exclusively with deep neural networks (DNNs) …

In the fields of deep learning and computer vision, the security of object detection models
has received extensive attention. Revealing the security vulnerabilities resulting from …

Deep neural networks (DNNs) have various applications owing to their feature learning
ability. However, recent studies have shown that DNNs are vulnerable to adversarial …

Deep learning-based visual sensing has achieved attractive accuracy but is shown
vulnerable to adversarial attacks. Specifically, once the attackers obtain the deep model …

Adversarial attacks in the physical world can harm the robustness of detection models.
Evaluating the robustness of detection models in the physical world can be challenging due …

Despite the high quality performance of the deep neural network in real-world applications,
they are susceptible to minor perturbations of adversarial attacks. This is mostly …

Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …

As Autonomous Vehicles (AVs) mature into viable transportation solutions, mitigating
potential vehicle control security risks becomes increasingly important. Perception modules …

We propose a new real-world attack against the computer vision based systems of
autonomous vehicles (AVs). Our novel Sign Embedding attack exploits the concept of …

Deep Neural Networks are built to generalize outside of training set in mind by using
techniques such as regularization, early stopping and dropout. But considerations to make …