Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks
for their everyday functions on the road so that safety of passengers and vehicles can be …

In this paper, we investigate" split-second phantom attacks," a scientific gap that causes two
commercial advanced driver-assistance systems (ADASs), Telsa Model X (HW 2.5 and HW …

The susceptibility of modern machine learning classifiers to adversarial examples has
motivated theoretical results suggesting that these might be unavoidable. However, these …

Computer vision applications like traffic monitoring, security checks, self-driving cars,
medical imaging, etc., rely heavily on machine learning models. It raises an essential …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Image denoising can remove natural noise that widely exists in images captured by
multimedia devices due to low-quality imaging sensors, unstable image transmission …

The evolution of automotive technology will eventually permit the automated driving system
on the vehicle to handle all circumstances. Human occupants will be just passengers. This …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Deep learning-based face recognition models are vulnerable to adversarial attacks. In
contrast to general noises, the presence of imperceptible adversarial noises can lead to …